Projects on(Social) Network Analysis

F.W. Takes

Leiden University, The Netherlands

Masterclass — September 16, 2015

Projects — Masterclass — September 16, 2015 1 / 7

Projects

Topics: social network analysis, network science, graphmining, data mining, data science, algorithms

Quantitative studies (so, with concrete algorithms, largereal-world datasets and experiments)

Project work in C++, Python or other Linux-friendly languages

Thesis in LATEX

Relevant courses

1 MSc: Social Network Analysis for Computer Scientists (orComplex Networks)

2 BSc: Algorithms, Data Structures and Data Mining

Interested? Contact me at takesfw@liacs.leidenuniv.nl orwalk by Snellius room 157b

Projects — Masterclass — September 16, 2015 2 / 7

Project 1: Corporate Network Analysis

Analyze global network of 10 million firms across the globe

Firms are connected due to board interlocks or ownership ties

Interdisciplinary project: computer scientists and social scientists

Approach: (social) network analysis techniques: centralitymeasures, community detection algorithms, etc.

Collaboration with University of Amsterdam, CORPNET project

Interested? takesfw@liacs.leidenuniv.nl or find me in 157b

Projects — Masterclass — September 16, 2015 3 / 7

Figure : Board interlock network (200 000 nodes and over 1 000 000 edges)

Project 2: Comparing Graph Frameworks

Many network analysis toolkits

Not all equally suitable for analyzing “big” network data

Compare toolkits: SNAP, NetworkX, Graph-Tool, . . .

Data: up to 100 social network datasets of different sizes

Literature review of suitable comparison measures

Comparison based on time and memory usage for certain criticaloperations (distance computation, component analysis, etc.), . . .

Interested? takesfw@liacs.leidenuniv.nl or find me in 157b

Projects — Masterclass — September 16, 2015 5 / 7

Project 3: Patent Network Analysis

Data: over 1 million European patent applications and relatedscientific publications

Question (broad): How can we automatically mine thepatterns in the patent data and scientific publication data,and what do these patterns tell us about how scientificpapers and patents are related?

Network modelling: patent citation networks, scientific citationnetwork and co-authorship networks

Approach: bit of text mining + network analysis techniques:centrality measures, community detection algorithms, etc.

Collaboration with Kenedict Analytics and CWTS Leiden

Interested? takesfw@liacs.leidenuniv.nl or find me in 157b

Projects — Masterclass — September 16, 2015 6 / 7