Visualizing Bibliographic Databases as Graphsand Mining Potential Research Synergies

Iraklis VarlamisDepartment of Informatics and Telematics

Harokopio University of AthensAthens, Greece

Email: varlamis@hua.gr

George TsatsaronisDepartment of Computer and Information ScienceNorwegian University of Science and Technology

Email: gbt@idi.ntnu.no

Abstract—Bibliographic databases are a prosperous field fordata mining research and social network analysis. They containrich information, which can be analyzed across different dimen-sions (e.g., author, year, venue, topic) and can be exploited inmultiple ways. The representation and visualization of biblio-graphic databases as graphs and the application of data miningtechniques can help us uncover interesting knowledge concern-ing potential synergies between researchers, possible matchingsbetween researchers and venues, or even the ideal venue forpresenting a research work. In this paper, we propose a novelrepresentation model for bibliographic data, which combines co-authorship and content similarity information, and allows for theformation of scientific networks. Using a graph visualization toolfrom the biological domain, we are able to provide comprehensivevisualizations that help us uncover hidden relations betweenauthors and suggest potential synergies between researchers orgroups.

I. I NTRODUCTION

Currently, vast amount of scientific publications are storedin online databases, such asDBLP, arXiv, andPubMed. Thesedatabases store rich information such as the publications titles,author(s), year, and venue. Less often they contain the abstractor the full publications’ content, and their references. Despitetheir rich content, bibliographic databases offer limited acces-sibility and do not efficiently exploit metadata elements. Theyusually restrict user queries to simple keyword-based searchand retrieve scientific publications that contain the query termsin the selected metadata elements. As a result, there is oftenlarge semantic gap in bibliographic search engines betweenusers’ needs and retrieved results, since access to the fullcontent of the papers, or even the abstracts, is often restricted.

The exploitation of additional semantics such as date, af-filiation, citations, co-citations, and co-authorship may furtherimprove search capabilities and create novel services for bibli-ographic databases. In this direction, semantic enabled searchengines for bibliographical data sources, such asGoPubMed[1], which specializes in the life sciences, overcome traditionalkeyword-based search problems and improve search results.In other cases, the increased popularity of social networksanalysis had a significant impact on deployed bibliographicdatabases search services. New databases have been published,offering online services that process publication metadata at

the maximum, such asArnetMiner1[2] or Microsoft AcademicSearch2. Authors and venues ranking, organization by year ortopic, author profiles extraction and authors name disambigua-tion are only some of the services provided on top of thesedatabases. Services that visualize co-authorship informationare also available, such as the”Instant graph search”3, whichpresents the existent co-authorship paths connecting two au-thors, or the”Social graph”4, which presents all the co-authorsof a single author in a star topology.

Despite the advantages of the semantic-enabled technolo-gies, an imminent implication of the restricted access to thefull articles information is that all research efforts towardsmining scientific communities and bibliographic databasesare restricted to accessing only the metadata offered by thebibliographic sources. Under these circumstances, mining bib-liographical databases in order to extract possible researchsynergies, identify research trends, and discover scientific the-matic cliques or co-authorships, are restricted to the processingof co-authorship or co-citation graphs. In this direction, wepropose in this paper a novel methodology for constructingand visualizing co-authorship graphs from bibliographicaldatabases, and show how these graphs can be mined to extractuseful information such as possible future research synergiesand strong collaboration links.

The suggested method is a two-level approach. At thefirst level, a co-authorship graph is constructed processingbibliographical data. The graph is then processed using anovel technique calledPower Graph Analysis[3], which wetransfer for the first time, to the best of our knowledge, fromthe bioinformatics domain to the processing of bibliographicalgraphs. ThroughPower Graph Analysis, a given graph’s nodesmay be clustered, throughcliquesandbicliquesrecognition inthe initial graph. The resultingPower Graphallows a veryefficient visualization of the authors graph, while in tandemidentifies cliques and bicliques of co-authors, representingthem with Power Nodes. At this stage, eachPower Nodeisessentially a set of authors that have written several paperstogether. At the second level, we augment thePower Graph

1http://www.arnetminer.org/2http://academic.research.microsoft.com/3Co-author Path in Microsoft Academic Search4Co-author Graph in Microsoft Academic Search

with edges betweenPower Nodesthat quantify the similaritybetween the authors’ sets, in terms of the similarity of thepapers’ titles written by the respective author set. This secondlevel, offers a richer representation of the initial co-authorshipgraph, which is visualized in an efficient manner. Finally, weshow how we can predict possible research synergies betweenauthors from this final augmented graph.

The rest of the paper is organized as follows: Section IIpresents some preliminary concepts regarding the constructionof graphs from bibliographical databases and the use ofPowerGraphs in the bioinformatics domain, and discusses relatedwork. Section III introduces our approach for mining po-tential research synergies from co-authorship graphs. SectionIV demonstrates our findings stemming from the applicationof our approach to bibliographic data. Finally, Section Vconcludes and provides pointers to future work.

II. PRELIMINARIES AND RELATED WORK

The primary focus of this work is the co-authorship informa-tion provided by bibliographic databases and the secondary isthe short (title) or extended (abstract or full paper) content thatpertains to each publication. Citation information is not consid-ered, since this is seldom available in bibliographic databases.Graph-based mining methods in bibliographic databases op-erate usually in three steps: (a) a graph is created usingauthors, conferences and papers’ topics, (b) application ofa graph-based partitioning or ranking algorithm takes place,and, (c) results are presented either in the form of nodeclusters, e.g., authors by topic, conferences by topic, or usinga graph visualization approach, e.g., co-authors of a singleauthor in a star topology. The majority of research works inbibliographic data mining aims at ranking authors or findingauthors’ communities. In this work we present a differentapproach which combines graph-based community miningwith text mining techniques in order to extract and visualizeuseful information from bibliographic data, such as potentialsynergies between researchers or research groups. In orderto provide a better understanding of how graphs are createdfrom bibliographic data and what are the visualization options,in the following we summarize the most important researchworks in the field and illustrate the different alternatives ineach process.

A. Constructing Graphs from Bibliographical Databases

Inspired by social network analysis, works on bibliograph-ical databases have proposed different alternatives for mod-elling bibliographic information using graphs. These can bedivided in two main categories: (a) methods that createn-partite graphs, which contain for instance authors, conferences,or topics as nodes, and edges that connect nodes of differenttype and represent relations (e.g., an author has published apaper in a conference), and (b) methods that create graphswith a single node type and edges that may vary in meaningdepending on the application.

In the former category, in [4] a bipartite model that connectsconferences to authors is proposed. Tripartite graph models for

authors-conferences-topics have also been introduced in thepast [2], [4]. In these cases the topics information is extractedfrom the paper titles and the resulting tripartite models expandthe authors-topics model presented in [5]. Finally, in [6]the authors perform domain specific author and conferenceranking by analyzing a bipartite author-conference graph usingclustering and ranking heuristics.

In the latter category, the graph nodes are usually theauthors, with the edges representing either citation or co-authorship relations between the connected nodes. The firstone is a directed citation graph [7], which is usually employedfor ranking authors, whereas the second is an undirected co-authorship graph, which is mainly used for finding authorcommunities (also known ascliques) [8], [9], but can also beemployed for measuringauthor centrality [10], [11], a typeof author importance. Some of the criteria that might be usedto weigh the edges of such author graphs are: number of co-authored papers, content similarity between their publications,number of co-citations or couplings, and number of commonconferences between the connected authors.

Another interesting type of graphs that can be constructedfrom bibliographical data are the co-authorhypergraphs,where each edge (hyperedge) corresponds to a publicationand connects all the co-authors of the specific publication.Author cliques can then be extracted from the graph [12].In this direction, in our previous work [13], we presented anapplication of co-authorhypergraphcreation and clustering ofits nodes.

B. Mining Bibliographical Graphs

Bibliographic data organization has attracted the applicationfocus of many data mining research works. In the case ofscientific community mining from publication records thechallenge is to discover research communities that sharecommon interests. In [14] a method is proposed that relieson the scientists’ publication records in order to create scien-tific communities. Moreover, community mining systems havebeen proposed in the past, which use bibliographic data inorder to discover and visualize researchers’ communities [4],[15].

In our previous work [13], we experimentally studied theuse of a novel semantic relatedness measure for the thematicorganization of research papers in an attempt to improve theeffectiveness of retrieval in bibliographic data. In particular, weused theOMIOTISmeasure [16], which captures the semanticrelatedness between text segments, and with its application weenabled the thematic organization of the bibliographic datastored in online databases. In this direction, of organizingbibliographical entries into thematic subsets based on textsimilarity, other research works have employed standard textclassification techniques, e.g.,Bayesianmethods orSupportVector Machines(SVM) [17], Concept Base Vector SpaceModels[18], in order to assign research papers into appropriatecategories. The combined utilization of metadata and full-text information for classifying bibliographic records into

(a) Huge biological ”fur ball” network.

(b) Before and after the application ofPower Graphs.

Fig. 1. An example of a huge biological network is shown in Figure1(a) [3]. In a smaller scale example (Figure 1(b)) the application ofPower Graphsdemonstrates how shared protein complexes can be easily identified in the producedPower Graph.

appropriate subject classes [19] has also been proposed in thepast.

Our work is complementary to the above research direc-tions. In this paper we propose a novel method to constructand mine the co-authorship graph, in order to identify potentialfuture research synergies. For this purpose, we use the simi-larity between authors from different communities, measuredon the context of their publications. In the graph creation step,we visualize the co-authorship communities using a techniquetransferred from the biomedical domain, namelyPower GraphAnalysis[3], and in the second step we enrich the constructedPower Graphwith similarity edges betweenPower Nodes,based on the text-to-text similarity between the authors’ papertitles. Our method is unsupervised, thus obviates the need forcollecting training data samples and its performance does notdepend on the quality of any type of training examples.

C. Visualizing Graphs with Power Graphs

In biology and bioinformatics studies, networks play acrucial role. Yet, their analysis and representation is a difficultproblem. Recent experimental and computational progressyields diverse networks of increased size and complexity. For

example there are networks of several types, such as small andlarge scaleinteraction networks, regulatory networks, geneticnetworks, protein-ligand interaction networks, and homologynetworksanalyzed and published regularly. A common wayto access the information in a network is though directvisualization, but this often fails as it just results in”fur balls”from which little insight can be gathered. On the other hand,clustering techniques manage to avoid the problems caused bythe large number of nodes and even larger number of edgesby keeping a coarse-grained level of the networks’ informationand, thus, abstracting details. But these fail too since, in fact,much of the biological information lies in the details. Similarrestrictions hold for bibliographic data and more specifically,for co-authorship graphs. The large number of authors andco-authorship edges makes the visualization task extremelydifficult.

In order to provide an efficient methodology for visualiz-ing large and complex biological networks, without loosinginformation, the authors in [3] present a novel methodologyfor analyzing and representing such networks, introducingPower Graphs. Power Graphsare a lossless representation

Fig. 2. The three basic motifs recognized byPower Graphs: Star, CliqueandBiclique. Power Nodesare sets of nodes andPower EdgesconnectPowerNodes. A Power Edgebetween twoPower Nodessignifies that all nodes ofthe first set are connected to all nodes of the second set. Nodes within aPowerNodeare not necessarily connected to each other.

of networks which reduces network complexity by explicitlyrepresentingre-occurring network motifs. Moreover, PowerGraphs can be clearly visualized, as they compress up to90% of the network’s edges and are applicable to all types ofnetworks such asprotein interaction, regulatory, or homologynetworks. Figure 1 shows two examples of”fur balls” in bio-logical networks: in the second example, which is small scale,the application ofPower Graphsresults into a visualizationwhere the shared protein complexes can be easily identified,whilst in the original network, this was impossible [20]. InFigure 2 the three basic motifs recognized byPower Graphs(i.e. Star, CliqueandBiclique) are shown. They constitute thebasic transformations to cluster the nodes of an original graphinto Power Nodes, connected withPower Edges.

In this paper, it is for the first time, to the best of our knowl-edge, thatPower Graphsare applied for mining informationfrom bibliographical graphs. In analogy to protein networksthat contain proteins as nodes and edges that represent their in-teractions, the co-authorship graphs contain information aboutauthors and their co-operations. As a consequence, finding re-occurring structural motifs in co-authorship graphs is a steptowards discovering author communities. Additionally, in thiswork, based on the contextual similarity of publications wediscover potential synergies between members from differentcommunities.

III. A PPROACH

A. Co-authorship Graphs withPower Graphs

The first decision regarding the creation of co-authorshipgraphs is on the type and meaning of edges. When a paperhas k authors, the two representation alternatives are either

TABLE IAN EXAMPLE OF A BIBLIOGRAPHIC RECORD.

paper − id Authors

p1 a1,a2,a3

p2 a1,a2,a4

p3 a1,a2

p4 a3,a4

p5 a3,a4

a1

a4

a2

a3

a1

a4

a2

a3

1

3

1

1

1

2

1

1

2

1

Fig. 3. The graph and hypergraph co-authorship models.

to add ahyperedgeconnecting thek author nodes, or to addsimple edges connecting each pairwise combination of thekauthors. SincePower Graphsdo not supporthyperedgeswework with the second alternative. However, usinghypergraphsand ahypergraphpartitioning algorithm [21] is another option.The second decision refers to the weighting of edges. Althoughmany existing studies on the co-authorship graphs model theco-authorship relation by an undirected and unweighed edge,in this work we want to model the strength of the relationbetween authors, by adding edge weights. An edge weightingscheme for the author graph has been also employed in [12],with very interesting results.

The resulting weighted co-authorship graph is formallymodelled as follows. Let the graphG = (V, WE), whereV isthe set of authors and a weighted edgewe= {ν1, ν2, wν1,ν2} ∈WE represents that authorsν1 and ν2 have co-authoredwν1,ν2 papers. Similarly, this representation can be usedif hyperedgesare used, as follows. LetG = (V, WHE),where V is the set of authors and a weightedhyperedgewhe= {ν1, ..., νn, wν1,...,νn

} ∈ WHE represents that authorsν1,..., νn have co-authoredwν1,...,νn

papers. An example of abibliographic record and the resulting co-authorship graph andhypergraphis depicted in Table I and Figure 3 respectively.

B. Power EdgesInformation

The most important contribution of thePower Graphmodelis its ability to group several nodes intoPower Nodesand toaggregate edges intoPower Edges. However, the knowledgethat can be extracted from each of the three mainPower Graphmotifs, namelystar, clique, and bi-clique may differ. In thestar motif a Power Edgeconnects an author with a set of co-authors. Theclique motif corresponds to a clique of authorsthat frequently publish papers together and the correspondingPower Edgeis a loop to thePower Nodeitself. Finally, in thebi-clique motif, a Power Nodegroups two or morestars andas a result thePower Edgeconnects two distinct author setswhose members have published papers together (one author

a20

a32

a21

a33

a22a34

a3a10

a9a8

a31

a7

a1

a4a5

a2

a6

Edges

------

a1 a3

a1 a4

a1 a5

a2 a3

a2 a4

a2 a5

a3 a6

a3 a7

a3 a8

a3 a9

a3 a10

a20 a31

a20 a32

a21 a31

a21 a33

a22 a31

a22 a34

Fig. 4. A sample co-authorshipPower Graph.

from each set).The usability of thecliquemotif is limited in our paradigm,

since all authors inside thePower Nodeof the clique havealready published a joint work in the past, so the synergy hasalready been materialized. However, a further analysis of astarmotif will probably reveal potential synergies. As mentionedbefore, thePower Nodein the star motif contains all the co-authors of a given author. All these authors have a commonpoint of reference and, consequently, if their interests match,they can form cooperations. However, findingbi-cliquesin theco-authorship graph is the most straightforward indication ofa potential research synergy. Each author in one set of thebi-clique has co-authored one or more papers with all authors inthe other set but not with the authors in his own set. Thismotif depicts a possible cooperation among authors insideeach Power Node. Finally, Power Graphssupport anothermotif which can be very useful in our application. Thisis the Power Node inclusion motif[20], where a PowerNodecontains anotherPower Nodeand several more distinctauthors. Theinclusion motif can be also exploited for hiddensynergy potentials. Figure 4 gives an example of co-authorshipinformation and the correspondingPower Graph.

When the size of eachPower Nodein the bi-clique or thesize of thePower Nodein thestar motif, or the externalPowerNode in the inclusion motif is large, then the authors insidethe Power Nodemust be examined in terms of similarity ofinterests, as in these cases possible future research synergiesmay be found. In our model, similarity of interests is modelledas the similarity between their published works (i.e., betweennodes that participated in thePower Edgecreation). Thealgorithmic details of the proposed method are presented inthe following section.

C. Algorithmic Description

The algorithmic description of our method given a publica-tion databaseD is presented in Algorithm 1. We assume thatauthors (the nodes in our graph) are in lexicographical order.The first step in our method (lines1 to 8) is the creation of

the co-authorship graphG from the database of papersD.As explained before, for each paperp in the database, a setof weighted edges is added (or updated) to the co-authorshipgraph. The second step (line9) is the application of thePowerGraph Analysisalgorithm to the original graphG and thecreation of thePower Graph PG, which comprisesPowerNodes(pn) that are either nested or formcliques, stars andbi-cliques. The details of thePower Graph Analysisalgorithmare available in [3]. The final step of the algorithm comprisesthe examination ofPower Edges(lines 10 to 14) and PowerNodes(lines 15 to 20) and results in a list ofPower Nodeswhich may contain potential research synergies.

Input : Database of papers D, empty graph G={V,WE}Output : A list of candidate Power Nodes CPN

1 foreach Paperp ∈ D do2 foreach Author a ∈ p.authors do3 foreach Author b ∈ p.authors, b 6= a do4 V.add(a);5 V.add(b);6 if WE.containsKey(E(a,b)) then7 WE.updateV alueOf(E(a,b));

8 else WE.put((E(a,b), 1));

9 PG{PN,PE} = PowerGraph(G);10 foreach Power Edgepe ∈ PE do11 if pe.node1 ∈ PN then12 CPN.add(node1);

13 if pe.node2 ∈ PN then14 CPN.add(node2);

15 foreach Power Nodepn ∈ PN do16 foreach noden ∈ pn.nodes do17 pntemp = a new empty power node ;18 if n /∈ PN then19 pntemp.add(n);20 CPN.add(pntemp);

Algorithm 1: The Enhanced Power Graph creation algorithm

D. Complexity and Implementation Issues

The computational complexity of our method is explainedin the following. We assume that the database containsmpapers written byn distinct authors, and that the resultingPower Graphcontainspn Power Nodes. The first step, which isthe creation of the initial co-authorship graph (G = {V, WE},whereV is a set of vertices andWE a map of edge-weightvalues), requires a single scan of the publication database.Given that the size of the graph does not exceed the size ofthe main memory, the complexity of the first step isO(m).

The second step, relies on thePower Graph algorithm,which is a two-phase procedure. In the first phase the algo-rithm identifies potentialPower Nodesusing aJaccard-basedsimilarity metric on the neighbors of each node and a similar-ity based hierarchical clustering algorithm. For example, the

similarity between two authors is maximum when they havewritten the same number of papers with the same co-authors.The second phase of thePower Graphalgorithm performs agreedy search forPower Edges, by examining the problemof minimizing thePower Graphstructure as an optimizationproblem. Since the details of the usedPower Graphalgorithmimplementation are not known5 we can simply assume thatits complexity is relative to the complexity of the hierarchicalalgorithm (O(n2log(n)) if the priority-queueHAC algorithmis implemented [22]), and to the complexity of the greedypower edge search algorithm, which is linear to the numberof Power Nodes(O(pn)).

The final step, performs pairwise comparisons (using thepaper title information) between authors in eachPower Nodethat is of interest (i.e.,Power Nodesthat are nested, or formbi-cliques, or belong to astar motif) as described previously.In the worst case the complexity of this step is linear to thetotal number ofPower Nodes(pn) and Power Edges(pe), inorder for all the possible motifs to be checked. As a result, thecomplexity of theEnhanced Power Graphcreation method isO(m + n2 · log(n) + pn+ pe+ pn). Given thatPower Graphreports an80% reduction to the number of edges and nodesthe resulting complexity isO(m + n2 · log(n)).

The output of the algorithm is a set ofPower Nodesfromthe original Power Graph, which contains authors that canpossibly co-operate in the future. The selectedPower Nodescan be highlighted in the visualization of thePower Graph, orgiven as input to the author matching module, which examinesauthor similarity of interests in terms of their papers’ context.

E. A Walkthrough Example

In this section we present a more detailed examinationof the example shown in Figure 4. For simplicity, in thisexample we assume that each paper has exactly two authorsand corresponds to a single edge. Authorsa1 and a2 haveexactly the same co-authors (a3, a4, a5) but have never co-operated. The same holds for authorsa3, a4 and a5 whohave never worked together. This is depicted by abi-cliquein the Power Graph. In addition to the preview, authora3has collaborated witha1, a2 and a6 to a10. For this reasonauthora3 forms astar with his co-authors, who form a pair ofnestedPower Nodes(a1, a2 is inside the greaterPower Node).Finally, all the co-authors ofa31 form astar, thePower Nodeof which is of potential interest. All other authors that haveco-operated with a single author are ignored. If a thresholdvalue is added on the size ofPower Nodesto be examined,we will be able to further distill the candidatePower Nodesand find more promising matches.

IV. EVALUATION AND RESULTS

A. Experimental Setup

In order to provide a demonstration of our method, weemployed theDBLP6 Computer Science Bibliography, which

5http://www.biotec.tu-dresden.de/research/schroeder/powergraphs/6http://www.informatik.uni-trier.de/∼ley/db/

comprises more than1.5 million publications. The databaseprovides for each indexed paper the authors, title, venue andyear of publication. The visualization of the complete graphwould not make any sense since theDBLP database containspublications from many different research fields. Thus, wehave selected subsets of theDBLP dataset, which comprisepapers published in the same conferences, and the sameyears. For the graphs’ presentation we provide two alternativevisualizations: one that contains all thePower NodesandEdgesand one that contains only the strongest edges.

Data processing is done as described in the previous sec-tions: (a) we create the initial co-authorship graph from theselected subset of publications, (b) we generate thePowerGraph from the initial graph, and, (c) we prune the weakestcomponents of thePower Graph in order to improve thereadability of the result. Finally, we present in details the mostinteresting structures in each power graph.

B. Results on theDBLP Data

1) The database conferences:In the first experiment, weprocess theDBLP publications from the top-5 conferences inDatabases7, namely:SIGMOD, VLDB, PODS, ICDE, ICDT.The subset contains11, 369 papers published since1969. Thepapers have been written by10, 524 authors. Several papershave more than two authors, and several author pairs haveco-authored more than one paper. In order to reduce thecomplexity and improve readability of the graph, we omitauthors that have written only one paper in any of theseconferences. The resulting graph finally contains3, 860 nodes(authors) and15, 382 edges (co-authorship entries).

After applying thePower Graphalgorithm, the resultinggraph shown in Figure 5 contains1, 601 power nodes and8, 572 power edges. A modified version of thePower Graph,where the weakestPower Edgeshave been pruned away ispresented in Figure 6 and uncovers interesting substructuresof the original graph. In Figures 7 and 8 we zoom on thePower Graphin order to present some of these structures. Thepotential research synergies must be searched in cases like theones we highlight: (a) in Figure 7 the co-authors of an authorwho has astar motif (e.g., the4 co-authors ofH.P. Kriegel, inbold face font), may co-operate with all other authors in thePower Nodeof the star motif (e.g., authors in italics), (b) inFigure 8 the authors in aPower Node(or bi-clique), which isnested in anotherPower Node(e.g.,C.Jermainand authors inbold face fonts), may co-operate with all other authors in theouterPower Node(e.g., authors in italics).

An additional piece of information that we can easily drawfrom Power Graphsare the authorcliques(e.g., the co-authorsof P. Kriegel in Figure 7) that correspond to authors who co-operate frequently. The cliques are easily distinguished fromgroups of authors that have co-operatively written severalpapers (e.g., the group of authors on the top of the bigPowerNode in Figure 8), which are closely placed in the graph butdo not form aPower Node.

7as provided by Microsoft Academic Search in http://academic.research.microsoft.com

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Kenn_Gardels

Thanos_Papadimitriou

H.-J._Schneider

James_Rucker

Gordon_C._Everest

Aaron_Watters

Alexander_Thomasian

Michael_J._Cahill

Eliezer_L._Lozinskii

Lina_Peng

Bret_Hull

K._Sundar_Das

Arding_Hsu

Ron_Musick

Ding-An_Chiang

Andrea_J._Borr

Gianfranco_R._Putzolu

Pierre_WolperMarianne_Baudinet

Anthony_J._Bonner

Cristina_Sirangelo

Silvio_Salza

Tadeusz_Morzy

Edwin_M._Knorr

Albert_Croker

Darrell_D._E._Long

Jehan-Francois_Paris

Boanerges_Aleman-Meza

Federico_Barbic

Fausto_Rabitti

Rajan_Suri

Peter_Honeyman

Kamran_Parsaye

Toshimi_Minoura

Arun_Jagatheesan

Nenad_Jukic

Fernando_de_Ferreira_Rezende

Robert_S._Epstein

S._N._Maheshwari

Anja_Theobald

Ralf_Schenkel

Michael_J._Fischer

Nancy_A._Lynch

Wei_Zhao

John_A._Stankovic

Rona_Machlin

Gonzalo_Navarro

Ravindra_Guravannavar

Mahesh_V._SoundalgekarShourya_Roy

Rob_Gerritsen

Howard_L._Morgan

Laurynas_Biveinis

Saneyasu_Yamaguchi

Masato_Oguchi

Roberto_F._Santos_Filho

Yuan_Ni

James_R._Driscoll

Sheau-Dong_Lang

Hiroshi_Arisawa

Takao_Miura

Yongdong_Wang

Rishiyur_S._Nikhil

Sang-Won_Lee

Bin_Cao

Antonio_Badia

Craig_Fields

David_Jefferson

Sriram_Mohan

Mohan_Ahuja

Volkert_Brosda

Babis_Theodoulidis

Ambuj_Shatdal

Kazuhiko_Mogi

Abhinav_Gupta

Jens_Teubner

Guido_Nerjes

R._Kent_Wenger

Stavros_Papastavrou Gerald_Madlmayr

Eliezer_Levy

Hye-Young_Paik

Ingolf_Geist

Pawel_Terlecki

Zhiqiang_Yang

Srini_Acharya

Pascal_Poncelet

Sudhanva_Gurumurthi

Ion_Stoica

Huajun_Chen

King-sun_Fu

Amy_J._Lee

Gianluigi_Greco

Maheswaran_Surendra

Gagan_Aggarwal

Josep_Domingo-Ferrer

Ronald_C._Unrau

Satish_M._Thatte

Werner_Obermair

Wei-Ying_Ma

David_A._Brant

Hemal_Khatri

Yoshinobu_Yamashita

Greg_Barish

Richard_Jeffers

Chialin_Chang

Pinar_Koksal

Doug_Doole

Siva_Ravada

Christian_Garcia-Arellano

Hussien_Aly

Liang_Huai_Yang

C._K._Kim

Ray_Shea

Dennis_R._McCarthy

Thorsten_Meyer

B._Gopinath

Jianping_Fan

Edward_Chan

Gomer_Thomas

Chang_Sheng

Chotirat_(Ann)_Ratanamahatana

Michael_Novak

Youzhong_Liu

Ani_Thakar

Steven_W._Schlosser

James_P._Davis

Sven_Hartmann

Rakesh_K._Sinha

Giovanna_Guerrini

Robert_Baumgartner

Zhengdao_Xu

Miyuki_Ono

Takeshi_Tokuyama

L._Peter_Deutsch

Alessandro_Campi

Huahai_He

Ebru_Kilic

Graham_N._C._Kirby

Na_Zhao

Emanuele_Santos

Sara_Comai

Wolfgang_Wilkes

Carl_Hauser

Ralf_Nikolai

Charles_Levine

Wen-Te_K._Lin

Ahmet_Bulut

Amy_Unruh

Robert_F._Murphy

Sarvjeet_Singh

Kenji_Suzuki

Jaime_G._Carbonell

Rajiv_Jauhari

Yue_Pan

Robert_L._Grossman

Yasuhiko_Morimoto

Giorgio_Terracina

Caetano_Traina_Jr.

Lubomir_Bic

Luc_Moreau

Gopalan_Arun

Anthony_Nguyen

Pradeep_Dubey

Yogesh_Simmhan

Mary_Shaw

Mounia_LalmasJooseok_Park

C._K._Tan

Andrew_Nierman

David_Mutchler

Yun_Chen

Manfred_Reichert

Nattakarn_Ratprasartporn

Raymond_Chi-Wing_Wong

William_Bridge

Meng_Chang_Chen

Sudha_Ram

Janos_Demetrovics

B._Aditya

Marco_Cadoli

N._Winterbottom

Fabien_De_Marchi

Yihong_Ding

Gene_Raichelson

Craig_W._Thompson

Asaf_Adi

Jose_Martinez

Dennis_Fogg

Christos_Doulkeridis

Kazuhiko_Ushio

Dushyanth_Narayanan

Gregory_D._Speegle

Danzhou_Liu

Ruxandra_Domenig

Toan_Luu

Shinji_Fujiwara

Guillaume_Raschia

Shun_Yan_Cheung

Arkady_B._Zaslavsky

Stefano_Rizzi

Jonathan_Po

Huy_T._Vo

Yiyao_Lu

Fabio_Grandi

Todd_Eavis

Binto_George

Yixin_Ruan

Lee_A._Hollaar

Domenico_Ferrari

Yannis_Katsis

Joseph_L._Hellerstein

Antoni_Martinez-Balleste

Jurgen_Frohn

Adegbemiga_Ola

Cindy_Xinmin_Chen

Francesco_Antonacci

William_F._Cody

Kareem_El_Gebaly

Pedro_Cruz

Mummoorthy_Murugesan

Frederic_Gingras

Martin_S._Feather

Gaurav_Vijayvargiya

Bernard_Traversat

Ghassan_Z._Qadah

Wolfgang_Lindner

Yangjun_Chen

Paul_Iglinski

Zhen_Liu

Alok_Kirpal

Ralph_Deters

Mary_Jane_Irwin

Jianhua_Feng

You-Chin_Fuh

Daniel_Deutch

Yoelle_S._Maarek

Hyunchul_Kang

Lawrence_D._Bergman

Ke_Yang

Lothar_F._Mackert

Wolfgang_Effelsberg

Subhash_Suri

Yiping_Ke

Tharam_S._Dillon

Linqi_Liu

Alle_IJbema

Michael_Gesmann

Norman_S._Matloff

Hongkun_Zhao

Konstantinos_Morfonios

Ronald_D._Bonnell

Andreas_Hutflesz

Karl_Denninghoff

Samy_A._Mahmoud

Grzegorz_Czajkowski

Charuta_Nakhe

Minsoo_Lee

Peter_Bumbulis

Holger_Meuss

Yukihiro_Morita

Cristina_Ribeiro

Cristiana_Amza

Ryan_Shipley

Robert_W._Irving

Jianchun_Fan

Florin_Rusu

Henrique_Paques

Zoltan_Somogyi

Jenny_Chiang

Sharon_McCure_Kuck

Svein_Erik_Bratsberg

Konrad_Stocker

Donald_Carney

Wei-Shinn_Ku

Richard_Swagerman

Eric_Sedlar

Pascal_Faudemay

Shuang_Hou

Sung-Hyuk_Cha

Thomas_Stohr

Sonny_Vaupel

Shigeo_Sugimoto

James_Harland

Manish_Bhide

David_Walker

Oliver_Seeliger

Neal_Sample

Georg_Walch

Paolino_Di_Felice

Claudio_T._Silva

Aparna_S._Varde

Ravi_Jampani

Giovambattista_Ianni

A._Khivesera

Timothy_W._Finin

Tomasz_Ksiezyk

Alberto_Marchetti-Spaccamela

Dushan_Z._Badal

Toshihide_Ibaraki

Tali_Yatzkar-Haham

G._Linderman

Noureddine_Mouaddib

Eric_M._Dashofy

Anton_P._Karadimce

Jun_Xu

Aris_Tsois

Jack_E._Shemer

Tobias_Kraft

Eugene_I._Lowenthal

Ryo_Taguchi

Victor_Teixeira_de_Almeida

Mingzhe_Gu

Houman_Alborzi

Yihua_Huang

Fabian_M._Suchanek

Elizabeth_J._O_Neil

Himawan_Gunadhi

Ira_Assent

Kou-Horng_Yang

Douglas_S._Kerr

Antonio_Albano

Yi_Deng

Ju-Hong_Lee

Uwe_Wloka

Arvind_Hulgeri

Steve_Lawrence

Fabrizio_Ferrandina

Ken-Chih_Liu

Rob_Bell

Robert_Fenk

Matteo_Golfarelli

Mingzhu_Wei

Zhiming_Ding

David_Botzer

Wilhelm_Hasselbring

Pavel_Avgustinov

Lin_Yu

Helen_M._Thomas

W._A._Walker

Khanh_Vu

Mei-Ling_Shyu

P._Dell_Orco

Yen-Kuang_Chen

Rodolphe_Michel

Kiyoshi_Ono

Jason_Chionh

Peter_Klahold

Witold_Lipski_Jr.

Myoung-Ho_Kim

Haruhiko_Kojima

Toon_Calders

Arvind_Sundararajan

Surojit_Chatterjee

Mustaque_Ahamad

Windsor_W._Hsu

Abdulmotaleb_El-Saddik

Ravi_S._Sandhu

Herman_Lam

Jeffrey_Shneidman

James_Cheng

Adam_Meyerson

Paul_M._Bober

Mario_A._Lopez

Abhirup_Chatterjee

M._K._Siu

Michel_Lacroix

Michael_Siegel

Elad_Verbin

Yu_Suzuki

Elaheh_Pourabbas

Jo-Mei_Chang

Luis_Leopoldo_Perez

Heiko_Muller

Gokhan_Ozhan

Seiichi_Kawazu

Margaret_H._Eich

Cem_Evrendilek

Tetsuo_Mizoguchi

Patrick_Kling

Philippe_Thiran

Brahim_Medjahed

Ian_T._Foster

Philip_L._LehmanIsmail_Hakki_Toroslu

Ahmet_Cosar

Alexander_Maedche

Jafar_Adibi

Lawrence_McNamee

Paul_Bird

Milan_Petkovic

Peter_P._Chen

Satoshi_Oyama

Va-On_Tam

Antonio_Picariello

Randal_J._Peters

Matthew_Huras

Umesh_Maheshwari

Peter_Bodorik

Vikas_Arora

Meikel_Poess

Ugur_Halici

Tatsuo_Tsuji

Anna_Cinzia_Squicciarini

Tam_Minh_Tran

Vijay_M._Sarathy

Sumit_Negi

Changqing_Li

Xinqiang_Zuo

P._Hopewell

Bianca_Schroeder

Bernd_J._Kramer

Arthur_J._Collmeyer

Jean-Marie_Nicolas

Peter_McIlroy

Esteban_Zimanyi

Scott_Shenker

Nancy_D._Griffeth

Anand_Bhaskar

Luigi_Pontieri

Xiaodan_Wang

Li_Ma

Robert_Demolombe

Nikolaus_Augsten

Charles_D._Cranor

Wei-bang_Chen

Andrew_Yu

David_C._Roberts

Shilpa_Lawande

Eugene_Inseok_Chong

Charles_C._Tappert

Apostolos_Papadopoulos

Hyoungmin_Park

Frank_K._H._A._Dehne

G._Jack_Lipovski

Bernard_K._Plagman

Mukesh_Mulchandani

J._Leo

Zhengli_Huang

Ulrich_Guntzer

Peter_Hitchcock

Carsten_Lund

Christian_Kalus

Michael_Merritt

Nipun_Agarwal

Gary_H._Sockut

Oystein_Torbjornsen

Alun_D._Preece

Michael_D._Lieberman

Ken_Higuchi

Qiang_Wang

Tracy_Pham

Adrian_Segall

Binling_Jin

Antonio_Turtur

Vincenzo_Moscato

Etzard_Stolte

Chengcui_Zhang

Liu_Wang

Roelof_van_Zwol

Sandor_Heman

S._Choo

Elias_Frentzos

Jean-Louis_Lassez

Chuck_Freiwald

Toshinari_Iida

Albert_Atserias

Fumitada_Itakura

Erich_M._Nahum

Rafik_Taouil

Ernest_Szeto

L._Zhang

Toshihiro_Uehara

Toni_Urpi

John_S._Baras

Kaushik_Dutta

Rui_Fang

Gregory_Buehrer

Seok-Lyong_Lee

Jean-Yves_Lingat

Srikanth_Bellamkonda

Ziv_Bar-Yossef

Omar_Boucelma

Thierry_Coupaye

Uwe_M._Borghoff

Chris_Mayfield

Daniel_Keren

Sotiris_Brakatsoulas

H._Z._YangMiyuki_Nakano

Ming-Yee_Lai

Ken_Meyer

Ramon_Lawrence

Timothy_Roscoe

Amol_Ghoting

Akhil_Kumar

Rohit_Jain

Hideto_Ikeda

Keunjoo_Kwon

Tao_Li

Nick_G._Duffield

Florian_Krebs

John_Shepherd

Christian_Convey

Kenji_Itoh

Hidenori_Itoh

Robert_J._Lindner

Silvia_Nittel

John_Riedl

Brian_Vickery

G._Barbara_Demo

Istvan_Cseri

Hasan_Davulcu

Carmem_S._Hara

John_L._Pfaltz

Antoni_Olive

Daniele_Gardy

Tobias_Scholl

Hirofumi_Matsuzawa

Jolly_Chen

P._Bruce_Berra

Donald_R._Deutsch

Carmen_Costilla

Sten_Andler

Yongjian_Fu

David_Chu

Balaji_Rathakrishnan

Yijian_Bai

Paul_Watson

Bob_Jenkins

Manfred_Hauswirth

Wei_Tang

Luigi_Palopoli

Susan_Kotsovolos

Haifeng_Liu

Liadan_O_Callaghan

David_Gibson

Mark_Roantree

Wojciech_Cellary

Christine_Tomlinson

Vincent_Aguilera

Ashok_Joshi

Claudia_Roncancio

Joachim_Thomas_II

Birger_Hansel

Helmut_Schmidt

Edward_Sciore

San-Yih_Hwang

Sebastian_Michel

Raghunath_Othayoth_Nambiar

J._Spruce_Riordon

Shin_ichi_Satoh

Ravi_Murthy

Matthias_Bender

Anupam_Joshi

Marina_Moscarini

Andre_Hernich

Sergio_Flesca

Mariana_Jbantova

Bruce_Hillyer

Ken_ichi_Ishikawa

Riccardo_Rosati

Gang_Zhou

Lotfi_Lakhal

Hiroshi_Ishikawa

A._M._Alashqur

Joshua_Spiegel

Simon_Kamenkovich

Peter_Kunath

Jens_Dittrich

Ning_An

Shinichi_Morishita

Euthimios_Panagos

Jens_Lechtenborger

Srikanta_Tirthapura

Gabriel_Pui_Cheong_Fung

Norman_May

Anisoara_Nica

Michael_Schrefl

Colette_Rolland

Victor_O._K._Li

Liuba_Shrira

Todd_C._Mowry

Jean_Ferrie

Susan_Darling_Urban

Lingzhi_Zhang

Mirette_S._Marzouk

Manolis_Koubarakis

Darrell_Woelk

Samir_Khuller

David_W._Stemple

Ralf_Hartmut_Guting

Tobias_Riemenschneider

Andrzej_Cichocki

Fig. 5. AuthorsPower Graph(top-5 database conferences).

Node_DZ

Node_BC Node_RK

Ming-Ling_Lo

Stelios_PaparizosJignesh_M._PatelNuwee_WiwatwattanaShurug_Al-Khalifa

Yuqing_Wu

Gunter_SaakeRalf-Detlef_Kutsche

Stefan_ConradFelix_Saltor

Eugene_AgichteinLuis_Gravano

Pranay_JainPanagiotis_G._Ipeirotis

Carl-Christian_KanneGuido_Moerkotte

Sven_Helmer

Wolfgang_WilkesGunter_Schlageter

Rainer_Unland

George_P._CopelandWilliam_Alexander

Tom_W._Keller

Kyu-Young_Whang

Hao_He

M._Tamer_Ozsu

Shivnath_BabuMayur_Datar

Itaru_Nishizawa

Yibei_Ling

Shankar_PalGideon_Schaller

Supiti_BuranawatanachokeKarl_Dias

Shantanu_Joshi

Jonathan_KleinBenoit_Dageville

Yujun_WangStratos_PapadomanolakisRomain_Colle

Leonidas_GalanisVenkateshwaran_Venkataramani

Uri_ShaftGraham_Wood

Liuba_ShriraUmesh_MaheshwariRobert_GruberAtul_Adya

Barbara_Liskov

Jennifer_Widom

Dmitri_V._KalashnikovSharad_Mehrotra

Nalini_VenkatasubramanianIosif_LazaridisInge_Thyssens

Marc_GyssensJan_Van_den_BusscheDirk_Van_GuchtMarc_Gemis

Jan_Paredaens

Feng_ShaoChavdar_BotevLin_Guo

Fan_Yang_0002

Xifeng_YanChen_Wang

Wenfei_Fan

W._Bruce_Croft

Peter_A._BonczStefan_Manegold

Sara_ComaiMarco_Brambilla

Maurizio_LenzeriniDiego_Calvanese

Xin_XuGao_Cong

Ravi_KrishnamurthyCharles_HochMichael_SuckowNita_Goyal

Brian_Meckler

Akrivi_Vlachou

Johannes_Gehrke

Christos_Doulkeridis

Daniela_Florescu

Enrico_Nardelli

Dan_Suciu

Ralf_Hartmut_Guting

Henry_F._Korth

Aoying_Zhou

Anthony_K._H._Tung

Xueqing_Gong

Kris_Ganjam

Eugene_J._ShekitaStratis_Viglas

Yucel_SayginVassilios_S._Verykios

H._V._JagadishLaks_V._S._LakshmananWei-Ying_MaJi-Rong_Wen

Donald_Kossmann

Leonid_LibkinLimsoon_Wong

Jayavel_Shanmugasundaram

Geoffrey_Phipps

Rajeev_Rastogi

Marcia_A._Derr

Rakesh_Agrawal

Daniel_R._Ries

Jiawei_Han

Wen-Te_K._Lin

Jian_PeiKarl_Schnaitter

Christoph_Koch

Neoklis_Polyzotis

Vassilis_AthitsosGeorge_Kollios

Zachary_G._Ives

Craig_A._Knoblock

Divesh_Srivastava

Steven_Minton

S._Seshadri

S._Sudarshan

Peter_McIlroy

Umeshwar_Dayal

Ke_WangGuozhu_Dong

Gerhard_Weikum

Wei_Wang

Mitch_Cherniack

Venkatesh_Ganti

David_W._Stemple

Daniel_J._Abadi

Chee_Yong_Chan

Minos_N._Garofalakis

Mingxi_WuFei_Xu

Chengkai_Li

Victor_Vianu

Luis_Leopoldo_PerezRavi_Jampani

Mingsheng_Hong

Theodore_Johnson

Sihem_Amer-Yahia

Beng_Chin_OoiKian-Lee_Tan

Alon_Y._HalevyAnHai_Doan

Stefano_CeriPiero_Fraternali

Surajit_Chaudhuri

David_ButtlerCalton_PuWei_Han

Ling_Liu

Patrick_E._O_NeilElizabeth_J._O_Neil

A._Prasad_SistlaOuri_Wolfson

Alan_J._DemersWalker_M._White

David_B._LometRoger_S._Barga

Wei_Tang

Gautam_Das

Abraham_Silberschatz

Yuri_Breitbart

Muralidhar_KrishnaprasadZhen_Hua_Liu

Vivek_R._NarasayyaSanjay_Agrawal

K._Selcuk_CandanDivyakant_Agrawal

Mark_RoantreeWilhelm_Hasselbring

Zhen_Zhang

Bin_He

Kevin_Chen-Chuan_Chang

Jun_ichi_TatemuraOliver_Po

Flip_Korn

Paolo_MerialdoPaolo_Atzeni

Giansalvatore_Mecca

S._MuthukrishnanGraham_Cormode

Igor_Tatarinov

P._P._S._Narayan

Philip_Bohannon

Eric_LoCarsten_Binnig

Alon_Y._LevyMary_F._Fernandez

Mong-Li_LeeWynne_Hsu

Bernard_K._PlagmanRobert_J._Lindner

Mariano_P._ConsensAlberto_O._Mendelzon

Alessandro_CampiDaniele_Braga

Philip_S._YuHaixun_Wang

Praveen_SeshadriTobias_Mayr

Nikos_MamoulisMan_Lung_Yiu

C._UnnikrishnanWilliam_Bohrer

Luc_BouganimPhilippe_Pucheral

Kaizhong_ZhangBruce_A._Shapiro

Jason_Tsong-Li_WangDennis_Shasha

Songting_Chen

Oliver_SpatscheckCharles_D._Cranor

Vladislav_Shkapenyuk

Antonios_DeligiannakisNick_Roussopoulos

Yannis_KotidisYannis_Sismanis

G._LindermanEuthimios_Panagos

S._ChooAlexandros_BilirisDavid_Schrader

William_O_Connell

Son_Dao

Jaewoo_Kang

Prakash_LingaAdina_Crainiceanu

Ashwin_Machanavajjhala

Maria_Luisa_SapinoV._S._Subrahmanian

Sibel_Adali

Vincent_Y._LumPeter_Dadam

Georg_WalchR._ErbePeter_Pistor

Jurgen_Gunauer

Alon_PilbergAnat_Eyal

Tova_MiloCatriel_Beeri

Jerry_FowlerAmy_Unruh

Tomasz_KsiezykMosfeq_Rashid

Vipul_KashyapAndrzej_Cichocki

Darrell_Woelk

Ray_SheaGale_MartinMarek_Rusinkiewicz

Roberto_J._Bayardo_Jr.Richard_S._Brice

Abdelsalam_HelalMarian_H._Nodine

Lei_Sheng

Andrew_WitkowskiSrikanth_Bellamkonda

Sankar_SubramanianAbhinav_Gupta

Nathan_Folkert

Tolga_Bozkaya

Hasan_M._JamilLiangyou_Chen

Francois_LlirbatFrancoise_Fabret

Lawrence_D._BergmanVittorio_Castelli

Chung-Sheng_LiJohn_R._Smith

Wolfgang_WonnerMichael_Gillmann

Dong_XinKaushik_Chakrabarti

Tim_SheardSubhasish_Mazumdar

Ming-Chien_ShanFabio_Casati

Hongjun_LuHaifeng_Jiang

Liying_SuiDayou_Zhou

Yi_DengNaphtali_Rishe

Wei_Sun

Bjorn_?or_JonssonLaurent_AmsalegTodd_J._GreenNicholas_E._Taylor

Val_Tannen

Manfred_HauswirthKarl_Aberer

Philippe_Cudre-Mauroux

Jagannathan_SrinivasanEugene_Inseok_Chong

Souripriya_Das

Magdalena_Balazinska

Alin_Deutsch

Vincent_Oria

Yannis_BatistakisMichalis_Vazirgiannis

Maria_Halkidi

Gustavo_AlonsoMichael_Duller

Alexander_L._WolfJonathan_E._Cook

Benjamin_G._Zorn

Terrance_SwiftDavid_Scott_Warren

Konstantinos_F._Sagonas

Raymond_T._NgAlex_Pang

J._SrinivasanNarain_H._Gehani

Christopher_ReJulie_Letchner

Martin_StaudtManfred_A._Jeusfeld

Mary_ShawStephen_N._Zilles

Arvola_ChanStephen_Fox

Cong_YuAdriane_Chapman

Babis_TheodoulidisBarbara_Pernici

Jaime_G._CarbonellCharles_Rich

Georg_GottlobReinhard_Pichler

Xuemin_LinMing_Hua

Richard_Hull

John_A._StankovicDonald_F._Towsley

Filippo_CacaceStefano_Crespi-Reghizzi

Kyoji_HirataYoshinori_Hara

Yizhou_SunZhijun_Yin

Marie-Anne_NeimatDonovan_A._Schneider

William_E._WeihlElliot_K._Kolodner

Nenad_JukicSusan_V._Vrbsky

Omran_A._BukhresJiansan_Chen

Takeshi_FukudaYasuhiko_Morimoto

Takeshi_TokuyamaShinichi_Morishita

Farshad_FotouhiWilliam_I._Grosky

Jorgen_HanssonMikael_Berndtsson

John_DoughtyKen_Meyer

Tirthankar_LahiriAshok_Joshi

Aya_SofferYoelle_S._Maarek

SungRan_Cho

Ian_T._FosterLuc_Moreau

Huy_T._VoClaudio_T._Silva

Carlos_Eduardo_ScheideggerJuliana_Freire

Nell_B._DaleAlfred_G._Dale

Morton_M._AstrahanEdward_B._Altman

P._L._FehderMichael_E._Senko

David_K._JeffersonAnn_Ellis_Bandurski

Barron_C._HouselNan_C._Shu

Xiao_QinRobert_L._Grossman

J._Wesley_GrahamJames_W._Welch

Cyril_U._OrjiJon_A._Solworth

Mehrdad_JahangiriCyrus_Shahabi

Michel_LacroixAlain_Pirotte

Christian_RichardColette_Rolland

Maurizio_RafanelliElaheh_Pourabbas

Giuseppe_SantucciTiziana_Catarci

Sridhar_Ramaswamy

Amol_Deshpande

Phillip_B._Gibbons

Swarup_AcharyaViswanath_Poosala

Suman_Kumar_NathSrinivasan_Seshan

Peer_Kroger

Stefan_BerchtoldChristian_Bohm

Bernhard_BraunmullerDaniel_A._Keim

Hans-Peter_Kriegel

Markus_M._BreunigJorg_Sander

Bernhard_SeegerThomas_Brinkhoff

Ralf_Schneider

Kaushik_DuttaDebra_E._VanderMeer

Anindya_DattaHelen_M._Thomas

Krithi_Ramamritham

Anil_BahumanSubhasri_Duttagupta

Xin_Zhang

Elke_A._RundensteinerKajal_T._ClaypoolLi_Chen

Hong_SuBrian_MurphyMukesh_Mulchandani

Angelika_Kotz_DittrichWerner_Dreyer

Duri_Schmidt

Manolis_Koubarakis

Christos_TryfonopoulosStratos_Idreos

Ronald_Fagin

Ravi_KumarD._Sivakumar

Mehmet_AltinelIlker_DurusoyCetin_Ozkan

Walid_G._ArefJeffrey_Scott_Vitter

Ahmed_K._ElmagarmidElisa_Bertino

Wensheng_Wu

Weiyi_MengKing-Lup_Liu

Clement_T._Yu

Zonghuan_WuHongkun_Zhao

Vijay_RaghavanYiyao_Lu

Timos_K._SellisLouiqa_Raschid

Chih-Chen_Lin

Pamela_DrewLyman_Do

Prabhu_Ram

Node_OR

Il-Yeol_SongYang-Sae_Moon

Nick_KoudasDavid_Walker

Kathleen_Fisher

Jan_RittingerTorsten_GrustJun_YangBadrish_Chandramouli

Peter_J._HaasVolker_Markl

Jialie_ShenAnne_H._H._Ngu

John_Shepherd

Junyi_Xie

Patrick_J._HayesL._Peter_Deutsch

Fernando_Berzal_GalianoNicolas_Marin

Ismailcem_Budak_Arpinar

Asuman_DogacPinar_Koksal

Subramanian_ArumugamChris_Jermaine

Alin_Dobra

Cem_Evrendilek

Gokce_LaleciYildiray_Kabak

Abhijit_Pol

Sema_Mancuhan

Cevdet_DengiSena_Nural

Gokhan_OzhanFatma_Ozcan

Ugur_Halici

Ebru_Kilic

Node_NY

Node_QG

Node_QS

Ryan_HuebschIon_Stoica

Prasad_DeshpandeAmit_Shukla

Yihong_Zhao

Jeong-Hyon_Hwang

Michael_J._Carey

Philip_A._Bernstein

Lawrence_A._Rowe

Bruce_G._Lindsay

Miron_Livny

Hector_Garcia-Molina

Sailesh_Krishnamurthy

Per-Ake_Larson

David_J._DeWitt

Raghu_Ramakrishnan

David_Maier

Jeffrey_F._Naughton

Jeffrey_D._Ullman

Boon_Thau_Loo

Berthold_Reinwald

Timothy_Roscoe

Vijayshankar_Raman

Joseph_M._HellersteinMichael_Stonebraker

Sergey_Melnik

Yannis_Papakonstantinou

John_JannottiYenel_Yildirim

Ameet_Kini

Qiong_Luo

Srinath_Shankar

Michael_L._Brodie

Ugur_Cetintemel

David_BeechErhard_Rahm

Anurag_MaskeyYing_Xing

Alex_Rasin

Samuel_Madden

C._MohanHamid_Pirahesh

Michael_J._Franklin

Stanley_B._Zdonik

Wolfgang_LehnerJingren_Zhou

Serge_Abiteboul

Jim_Gray

Yanif_AhmadOlga_Papaemmanouil

Rajeev_MotwaniSvetlozar_Nestorov

George_LapisRoberta_Cochrane

Peter_Zabback

Guy_M._Lohman

Jin_LiVassilis_PapadimosKristin_Tufte

Peter_A._Tucker

Mian_LuRui_Fang

Pedro_V._SanderNaga_K._GovindarajuBingsheng_He

Ke_Yang

Kevin_S._BeyerR._Kent_Wenger

Jussi_MyllymakiShilpa_LawandeDonko_Donjerkovic

Guangshun_Chen

Feng_TianJianjun_Chen

Sabine_MassmannHong_Hai_Do

Gurmeet_Singh_MankuSridhar_Rajagopalan

Hongfei_GuoJonathan_Goldstein

Brad_AdelbergBen_Kao

Curt_J._EllmannGang_Luo

Vasilis_VassalosRamana_Yerneni

Nesime_Tatbul

T._Y._Cliff_Leung

Wolfgang_Kafer

Jim_Melton

Andrew_Eisenberg

James_Clifford

Ramez_Elmasri

Arie_Segev

Christian_S._Jensen

Nikos_A._Lorentzos

Jan-Eike_Michels

Peter_M._Schwarz

Vanja_Josifovski

Ilsoo_Ahn

Fabio_Grandi

Don_S._Batory

Nick_Kline

Gad_Ariav Curtis_E._DyresonJohn_F._Roddick

Michael_D._Soo

Krishna_G._Kulkarni

Richard_T._Snodgrass

Suryanarayana_M._Sripada

Fig. 6. Pruned authorPower Graph(top-5 database conferences).

2) Multi-disciplinary graphs:In the second experiment weattempt to visualize thePower Graphof two research com-munities from different disciplines, namely computer graphicsand information retrieval. More specifically we select papersthat have been published inSIGGRAPHand SIGIR, the topconferences in computer graphics and information retrieval re-spectively. The selected subset comprises3, 568 papers writtenby 5, 386 authors. Following the same author pruning strategy,we produce a graph that contains1, 303 nodes (authors) and2, 769 edges (co-authorship entries). The respectivePowerGraphshown in Figure 9 contains1, 391 Power Edgesand523Power Nodes, and forms two distinct, compact graph regions(IR community is on the left andGraphicscommunity on theright). All the small subgraphs between the two main regionsbelong to one or other field, do not connect to thePower

Henry_F._Korth

Rajeev_RastogiS._Seshadri

S._Sudarshan

Peter_McIlroy

Chengkai_LiAbraham_Silberschatz

Yuri_BreitbartZhen_Zhang

Bin_He

Kevin_Chen-Chuan_Chang

P._P._S._Narayan

Philip_Bohannon

Sridhar_Ramaswamy

Amol_Deshpande

Phillip_B._Gibbons

Swarup_AcharyaViswanath_Poosala

Suman_Kumar_Nath

Srinivasan_Seshan

Peer_Kroger

Stefan_Berchtold

Christian_Bohm

Bernhard_BraunmullerDaniel_A._Keim

Hans-Peter_Kriegel

Markus_M._Breunig

Jorg_Sander

Bernhard_Seeger

Thomas_Brinkhoff

Ralf_Schneider

Fig. 7. Part of the pruned authorPower Graph(top-5 database conferences).Star motif

Ismailcem_Budak_Arpinar

Asuman_DogacPinar_Koksal

Subramanian_Arumugam

Chris_Jermaine

Alin_Dobra

Cem_Evrendilek

Gokce_Laleci

Yildiray_KabakAbhijit_Pol

Sema_Mancuhan

Cevdet_Dengi

Sena_NuralGokhan_Ozhan

Fatma_Ozcan

Ugur_Halici

Ebru_Kilic

Fig. 8. Part of the pruned authorPower Graph(top-5 database conferences).Power Node nesting motif.

Nodesof each subgraph and have been placed by thePowerGraphdrawing module in between the two sub graphs only forpresentation purposes (i.e., they are not specialPower Nodesthat lie between the two research fields).

3) Measuring similarity based on content:In this finalexperiment, we further examine the cases of possible co-operation between authors by measuring their similarity ofinterests based on the titles of their publication record. Weemploy theOMIOTIS measure [16] and the methodology we

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Fig. 9. Pruned authorPower Graph(SIGIRandSIGGRAPHconferences).

presented in [13]. For each candidate pair of authors we mea-sure the average semantic relatedness between their publishedwork in the respective conferences, then we sort candidateauthor pairs in decreasing similarity score. The candidate pairsare selected as described in Section IV-B1, taking care toremove candidates that have already collaborated in the past.

The top results for the database conferences subset arepresented in Table II. Authors in the first positions havecommon co-authors, but have not co-authored a paper yet.A manual examination of their publication record reveals thattheir interests match. For example the first pair of authorsworks onBusiness Process Modelling, the second pair workson Privacy, and the third onSQL server’s optimization.

TABLE IITOP CANDIDATE PAIRS, RANKED BY SIMILARITY

Author A Author B similarityDaniel Deutch AnatEyal 0.222

Kristen LeFevre AlexandreV, Evfimievski 0.179Ming-Chuan Wu SteveHerbert 0.156

Babu Krishnaswamy AleksandrasSurna 0.154Alon Y, Halevy ChenLi 0.152

Ming-Chuan Wu AleksandrasSurna 0.148Jorg Sander DanielA, Keim 0.139

Conor Cunningham SteveHerbert 0.138SandeepanBanerjee AnandManikutty 0.136

Yanif Ahmad MagdalenaBalazinska 0.135

V. CONCLUSIONS

In this paper, we have introduced a novel approach forthe organization and the efficient presentation of bibliographicdatabase contents. The contribution of our approach lies onthe use of a graph reduction method that facilitates theefficient visualization of the dense co-authorship graph, theidentification of potential research synergies based on theanalysis of thePower Graph, and the ranking of potential co-author pairs by similarity of interests. More specifically, wehave demonstrated how the use ofPower Graph Analysiscanuncover potential future research synergies between authors.This modular approach helps us to avoid the burden offinding the optimal clustering and classification scheme forbibliographic data organization. As a proof of concept, wedemonstrated some of the capabilities of our approach in theDBLP data and we believe that it can be fruitfully exploredin several other data mining tasks. It is on our next plans toapply the same approach to more bibliographic networks aswell as to other social networks, and to study the evolutionof the graphs over time based on the comparison of differentgraph snapshots taken in different years.

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