hierarchical graph convolutional networks for semi

Hierarchical Graph Convolutional Networksfor Semi-supervised Node Classification

Fenyu Hu1,2∗ , Yanqiao Zhu1,2∗ , Shu Wu1,2† , Liang Wang1,2 and Tieniu Tan1,2

1 University of Chinese Academy of Sciences2 Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern

Recognition, Institute of Automation, Chinese Academy of Sciences{fenyu.hu,yanqiao.zhu}@cripac.ia.ac.cn, {shu.wu,wangliang,tnt}@nlpr.ia.ac.cn

Abstract

Graph convolutional networks (GCNs) have beensuccessfully applied in node classification tasks ofnetwork mining. However, most of these mod-els based on neighborhood aggregation are usuallyshallow and lack the “graph pooling” mechanism,which prevents the model from obtaining adequateglobal information. In order to increase the recep-tive field, we propose a novel deep HierarchicalGraph Convolutional Network (H-GCN) for semi-supervised node classification. H-GCN first repeat-edly aggregates structurally similar nodes to hyper-nodes and then refines the coarsened graph to theoriginal to restore the representation for each node.Instead of merely aggregating one- or two-hopneighborhood information, the proposed coarsen-ing procedure enlarges the receptive field for eachnode, hence more global information can be cap-tured. The proposed H-GCN model shows strongempirical performance on various public bench-mark graph datasets, outperforming state-of-the-artmethods and acquiring up to 5.9% performance im-provement in terms of accuracy. In addition, whenonly a few labeled samples are provided, our modelgains substantial improvements.

1 IntroductionGraphs nowadays become ubiquitous owing to the abilityto model complex systems such as social relationships, bi-ological molecules, and publication citations. The problemof classifying graph-structured data is fundamental in manyareas. Besides, since there is a tremendous amount of un-labeled data in nature and labeling data is often expensiveand time-consuming, it is often challenging and crucial toanalyze graphs in a semi-supervised manner. For instance,for semi-supervised node classification in citation networks,where nodes denote articles and edges represent citation, thetask is to predict the label of every article with only a fewlabeled data.

∗The first two authors contributed equally to this work.†To whom correspondence should be addressed.

As an efficient and effective approach to graph analysis,network embedding has attracted a lot of research interests.It aims to learn low-dimensional representations for nodeswhilst still preserving the topological structure and node fea-ture attributes. Many methods have been proposed for net-work embedding, which can be used in the node classificationtask, such as DeepWalk [Perozzi et al., 2014] and node2vec[Grover and Leskovec, 2016]. They convert the graph struc-ture into sequences by performing random walks on thegraph. Then, the proximity between the nodes can be cap-tured based on the co-occurrence statistics in these sequences.But they are unsupervised algorithms and cannot performnode classification tasks in an end-to-end fashion. Unlike pre-vious random-walk-based approaches, employing neural net-works on graphs has been studied extensively in recent years.Using an information diffusion mechanism, the graph neuralnetwork (GNN) model updates states of the nodes and prop-agate them until a stable equilibrium [Scarselli et al., 2009].Both of the highly non-linear topological structure and nodeattributes are fed into the GNN model to obtain the graph em-bedding. Recently, there is an increasing research interest inapplying convolutional operations on the graph. These graphconvolutional networks (GCNs) [Kipf and Welling, 2017;Velickovic et al., 2018] are based on the neighborhood aggre-gation scheme which generates node embedding by combin-ing information from neighborhoods. Comparing with con-ventional methods, GCNs achieve promising performance invarious graph analytical tasks such as node classification andgraph classification [Defferrard et al., 2016] and has showneffective for many application domains, for instance, recom-mendation [Wu et al., 2019; Cui et al., 2019], traffic fore-casting [Yu et al., 2018], and action recognition [Yan et al.,2018].

Nevertheless, GCN-based models are usually shallow andlack the “graph pooling” mechanism, which restricts the scaleof the receptive field. For example, there are only two lay-ers in GCN [Kipf and Welling, 2017]. As each graph con-volutional layer acts as the approximation of aggregation onthe first-order neighbors, the two-layer GCN model only ag-gregates information from two-hop neighborhoods for eachnode. Because of the restricted receptive field, the model hasdifficulty in obtaining adequate global information. How-ever, it has been observed from the reported results [Kipfand Welling, 2017] that simply adding more layers will de-

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Multi-channel GCNs

Coarsening operation

Refining operation

Softmax classifier

Shortcut connection

n1 × d n2 × d n3 × d n3 × dn2 × d

n1 × d

Figure 1: The workflow of H-GCN. In this illustration, there areseven layers with three coarsening layers, three symmetric refininglayers, and one output layer. Coarsening layer at level i producesgraph Gi+1 of ni+1 hyper-nodes with d-dimensional latent repre-sentations, vice versa for refining layers.

grade the performance. As explained in [Li et al., 2018], eachGCN layer acts as a form of Laplacian smoothing in essence,which makes the features of nodes in the same connectedcomponent similar. Thereby, adding too many convolutionallayers will result in the output features over-smoothed andmake them indistinguishable. Meanwhile, deeper neural net-works with more parameters are harder to train. Althoughsome recent methods [Chen et al., 2018; Xu et al., 2018;Ying et al., 2018] try to get the global information throughdeeper models, they are either unsupervised models or needmany training examples. As a result, they are still not ca-pable of solving the semi-supervised node classification taskdirectly.

To this end, we propose a novel architecture of HierarchicalGraph Convolutional Networks, H-GCN for brevity, for nodeclassification on graphs1. Inspired from the flourish of apply-ing deep architectures and the pooling mechanism into im-age classification tasks, we design a deep hierarchical modelwith coarsening mechanisms. The H-GCN model increasesthe receptive field of graph convolutions and can better cap-ture global information. As illustrated in Figure 1, H-GCNmainly consists of several coarsening layers and refining lay-ers. For each coarsening layer, the graph convolutional oper-ation is first conducted to learn node representations. Then,a coarsening operation is performed to aggregate structurallysimilar nodes into hyper-nodes. After the coarsening opera-tion, each hyper-node represents a local structure of the origi-nal graph, which can facilitate exploiting global structures onthe graph. Following coarsening layers, we apply symmetricgraph refining layers to restore the original graph structure fornode classification tasks. Such a hierarchical model managesto comprehensively capture nodes’ information from local toglobal perspectives, leading to better node representations.

The main contributions of this paper are twofold. Firstly,to the best of our knowledge, it is the first work to design adeep hierarchical model for the semi-supervised node classifi-cation task. Compared to previous work, the proposed model

1To make our results reproducible, all relevant source codes arepublicly available at https://github.com/CRIPAC-DIG/H-GCN.

consists of more layers with larger receptive fields, which isable to obtain more global information through the coarsen-ing and refining procedures. Secondly, we conduct extensiveexperiments on a variety of public datasets and show that theproposed method constantly outperforms other state-of-the-art approaches. Notably, our model gains a considerable im-provement over other approaches with very few labeled sam-ples provided for each class.

2 Related WorkIn this section, we review some previous work on graph con-volutional networks for semi-supervised node classification,hierarchical representation learning on graphs, and graph re-duction algorithms.

Graph Convolutional NetworksIn the past few years, there has been a surge of applying con-volutions on graphs. These approaches are essentially basedon the neighborhood aggregation scheme and can be furtherdivided into two branches: spectral approaches and spatialapproaches.

The spectral approaches are based on the spectral graphtheory to define parameterized filters. Bruna et al. (2014)first define the convolutional operation in the Fourier domain.However, its heavy computational burden limits the appli-cation to large-scale graphs. In order to improve efficiency,Defferrard et al. (2016) propose ChebNet to approximate theK-polynomial filters by means of a Chebyshev expansion ofthe graph Laplacian. Kipf and Welling (2017) further sim-plify the ChebNet by truncating the Chebyshev polynomial tothe first-order neighborhood. DGCN [Zhuang and Ma, 2018]uses random walks to construct a positive mutual informationmatrix. Then, it utilizes that matrix along with the graph’sadjacency matrix to encode both local consistency and globalconsistency.

The spatial approaches generate node embedding by com-bining the neighborhood information in the vertex domain.MoNet [Monti et al., 2017] and SplineCNN [Fey et al., 2018]integrate the local signals by designing a universe patch op-erator. To generalize to unseen nodes in an inductive setting,GraphSAGE [Hamilton et al., 2017] samples a fixed numberof neighbors and employs several aggregation functions, suchas concatenation, max-pooling, and LSTM aggregator. GAT[Velickovic et al., 2018] introduces the attention mechanismto model different influences of neighbors with learnable pa-rameters. Gao et al. (2018) select a fixed number of neigh-borhood nodes for each feature and enables the use of regularconvolutional operations on Euclidean spaces. However, theabove two branches of GCNs are usually shallow and cannotobtain adequate global information as a consequence.

Hierarchical Representation Learning on GraphsSome work has been proposed for learning hierarchical in-formation on graphs. Chen et al. (2018) and Liang et al.(2018) use a coarsening procedure to construct a coarsenedgraph of smaller size and then employ unsupervised meth-ods, such as DeepWalk [Perozzi et al., 2014] and node2vec[Grover and Leskovec, 2016] to learn node embedding based

https://github.com/CRIPAC-DIG/H-GCN

on that coarsened graph. Then, they conduct a refining pro-cedure to get the original graph embedding. However, theirtwo-stage methods are not capable of utilizing node attributeinformation and can neither perform node classification taskin an end-to-end fashion. JK-Nets [Xu et al., 2018] proposesgeneral layer aggregation mechanisms to combine the out-put representation in every GCN layer. However, it can onlypropagate information across edges of the graph and are un-able to aggregate information hierarchically. Therefore, thehierarchical structure of the graph cannot be learned by JK-Nets. To solve this problem, DiffPool [Ying et al., 2018]proposes a pooling layer for graph embedding to reduce thesize by a differentiable network. As DiffPool is designed forgraph classification tasks, it cannot generate embedding forevery node in the graph; hence it cannot be directly appliedin node classification scenarios.

Graph ReductionMany approaches have been proposed to reduce the graphsize without losing too much information, which facilitatedownstream network analysis tasks such as community dis-covery and data summarization. There are two main classesof methods that reduce the graph size: graph sampling andgraph coarsening. The first category is based on graph sam-pling strategy [Papagelis et al., 2013; Hu and Lau, 2013;Chen et al., 2017], which might lose key information dur-ing the sampling process. The second category applies graphcoarsening strategies that collapse structure-similar nodesinto hyper-nodes to generate a series of increasingly coarsergraphs. The coarsening operation typically consists of twosteps, i.e. grouping and collapsing. At first, every nodeis assigned to groups in a heuristic manner. Here a grouprefers to a set of nodes that constitute a hyper-node. Then,these groups are used to generate a coarser graph. For anunmatched node, Hendrickson and Leland (1995) randomlyselect one of its un-matched neighbors and merge these twonodes. Karypis and Kumar (1998) merge the two un-matchednodes by selecting those with the maximum weight edge.LaSalle and Karypis (2015) use a secondary jump duringgrouping.

However, these graph reduction approaches are usuallyused in unsupervised scenarios, such as community detectionand graph partition. For semi-supervised node classificationtasks, existing graph reduction methods cannot be used di-rectly, as they are not capable of learning complex attributiveand structural features of graphs. In this paper, H-GCN con-ducts graph reduction for non-Euclidean geometry like thepooling mechanism for Euclidean data. In this sense, ourwork bridges graph reduction for unsupervised tasks to thepractical but more challenging semi-supervised node classifi-cation problems.

3 The Proposed Method3.1 PreliminariesNotations and Problem DefinitionFor the input undirected graph G1 = (V1, E1), where V1 andE1 are respectively the set of n1 nodes and e1 edges, letA1 ∈ Rn1×n1 be the adjacency matrix describing its edge

weights and X ∈ Rn1×d1 be the node feature matrix, whered1 is the dimension of the attributive features. We use edgeweights to indicate connection strengths between nodes. Forthe H-GCN network, the graph fed into the ith layer is repre-sented as Gi with ni nodes. The adjacency matrix and hiddenrepresentation matrix of Gi are represented by Ai ∈ Rni×ni

and Hi ∈ Rni×di respectively.Since coarsening layers and refining layers are symmetri-

cal, Ai is identical to Al−i+1, where l is the total number oflayers in the network. For example, in the seven-layer modelillustrated in Figure 1, G3 is the input graph for the third layerand G5 is the resulting graph from the fourth layer. After onecoarsening operation and one refining operation, G3 and G5share exactly the same topological structure A3. As nodeswill be assigned as a hyper-node, we define node weight asthe number of nodes contained in a hyper-node.

Given the labeled node set VL containing m � n1 nodes,where each node vi ∈ VL is associated with a label yi ∈ Y ,our objective is to predict labels of V\VL.

Graph Convolutional NetworksGraph convolutional networks achieve promising generaliza-tion in various tasks and our work is built upon the GCN mod-ule. At layer i, taking graph adjacency matrix Ai and hiddenrepresentation matrix Hi as input, each GCN module outputsa hidden representation matrix Gi ∈ Rni×di+1 , which is de-scribed as:

Gi = ReLU(D− 1

2i AiD

− 12

i Hiθi

), (1)

where H1 = X , ReLU(x) = max(0, x), adjacency matrixwith self-loop Ai = Ai + I , Di is the degree matrix of Ai,and θi ∈ Rdi×di+1 is a trainable weight matrix. For easeof parameter tuning, we set output dimension di = d for allcoarsening and refining layers throughout this paper.

3.2 The Overall ArchitectureFor a H-GCN network of l layers, the ith graph coarseninglayer first conducts a graph convolutional operation as for-mulated in Eq. (1) and then aggregates structurally similarnodes into hyper-nodes, producing a coarser graph Gi+1 andnode embedding matrix Hi+1 with fewer nodes. The cor-responding adjacent matrix Ai+1 and Hi+1 will be fed intothe (i + 1)th layer. Symmetrically, the graph refining layeralso performs a graph convolution at first and then refinesthe coarsened graph to restore the finer graph structure. Inorder to boost optimization in deeper networks, we add short-cut connections [He et al., 2016] across each coarsened graphand its corresponding refined part.

Since the topological structure of the graph changes be-tween layers, we further introduce a node weight embed-ding matrix Si, which transforms the number of nodes con-tained in each hyper-node into real-valued vectors. Both ofthe node weight embedding and Hi will be fed into the ithlayer. Besides, we add multiple channels by employing dif-ferent GCNs to explore different feature subspaces.

The graph coarsening layers and refining layers altogetherintegrate different levels of node features and thus avoid over-smoothing during repeated neighborhood aggregation. Af-ter the refining process, we obtain a node embedding matrix

Hl−1 ∈ Rn1×d, where each row represents a node represen-tation vector. In order to classify each node, we apply anadditional GCN module followed by a softmax classifier onHl−1.

3.3 The Graph Coarsening LayerEvery graph coarsening layer consists of two steps, i.e. graphconvolution and graph coarsening. A GCN module is firstlyused to extract structural and attributive features by aggregat-ing neighborhoods’ information as described in Eq. (1). Forthe graph coarsening procedure, we design the following twohybrid grouping strategies to assign nodes with similar struc-tures into a hyper-node in the coarser graph. We first conductstructural equivalence grouping, followed by structural simi-larity grouping.Structural equivalence grouping (SEG). If two nodesshare the same set of neighbors, they are considered to bestructurally equivalent. We then assign these two nodes to bea hyper-node. For example, as illustrated in Figure 2, nodesBandD are structurally equivalent, so these two nodes are allo-cated as a hyper-node. We mark all these structurally equiva-lent nodes and leave other nodes unmarked to avoid repetitivegrouping operation on nodes.Structural similarity grouping (SSG). Then, we calculatethe structural similarity between the unmarked node pairs(vj , vk) as the normalized connection strength s(vj , vk):

s(vj , vk) =Ajk√

D(vj) ·D(vk), (2)

where Ajk is the edge weight between vj and vk, and D(·) isthe node weight.

We iteratively take out an unmarked node vj and calcu-late normalized connection strengths with all its unmarkedneighbors. After that, we select its neighbor node vk whichhas the largest structural similarity to form a new hyper-nodeand mark the two nodes. Particularly, if one node is left un-marked and all of its immediate neighbors are marked, it willbe marked as well and constitutes a hyper-node by itself. Forexample, in Figure 2, node pair (C,E) has the largest struc-tural similarity, so they are grouped together to form a hyper-node. After that, since only node A remains unmarked, itconstitutes a hyper-node by itself.

Please note that if we take out unmarked nodes in a dif-ferent order, the resulting hyper-graph will be different. Thelater we take a node out, the less its neighbors will be leftunmarked. So, for a node with fewer neighbors, it has fewerprobabilities to be grouped when it is taken out late. There-fore, we take out the unmarked nodes in ascending order ac-cording to the number of neighbors.

Using the above two grouping strategies, we are able to ac-quire all the hyper-nodes. For one hyper-node vi, its edgeweight to vj is the summation over edge weights of vj’sneighbor nodes contained in vi. The updated node weightsand edge weights will be used in Eq. (2) in the next coarsen-ing layer.

In order to help restore the coarsened graph to origi-nal graph, we preserve the grouping relationship betweennodes and their corresponding hyper-nodes in a matrix Mi ∈

A

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A2 = M>1 A1M1 =

0@

0 2 12 0 21 2 2

1A


Figure 2: The graph coarsening operation of a toy graph. Num-bers indicate edge weights and nodes in shadow are hyper-nodes. InSEG, node B and D share the same neighbors, so they are groupedinto a hyper-node. In SSG, node C and E are grouped because theyhave the largest normalized connection weight. Node A constitutesa hyper-node by itself since it remains unmarked.

Rni×ni+1 . Formally, at layer i, entry mjk in the groupingmatrix Mi is calculated as:

mjk =

{1, if vj in Gi is grouped into vk in Gi+1;0, otherwise. (3)

An example of the coarsening operation on a toy graph isgiven in Figure 2. Note that m11 = 1 in this illustration,since node A constitutes its hyper-node by itself. Next, thehidden node embedding matrix is determined as:

Hi+1 =M>i ·Gi. (4)

In the end, we generate a coarser graph Gi+1, whose adja-cency matrix can be calculated as:

Ai+1 =M>i ·Ai ·Mi. (5)

The coarser graph Gi+1 along with the resulting represen-tation matrix Hi+1 will be fed into the next layer as input.The resulting node embedding to generate in each coarseninglayer will then be of lower resolution. The graph coarseningprocedure is summarized in Algorithm 1.

3.4 The Graph Refining LayerTo restore the original topological structure of the graph andfurther facilitate node classification, we stack the same num-bers of graph refining layers as coarsening layers. Like thecoarsening procedure, each refining layer contains two steps,namely generating node embedding vectors and restoringnode representations.

To learn a hierarchical representation of nodes, a GCN isemployed at first. Since we have saved the grouping relation-ship in the grouping matrix during the coarsening process, weutilize Ml−i to restore the refined node representation ma-trix of layer i. We further employ residual connections be-tween the two corresponding coarsening and refining layers.In summary, node representations are computed by:

Hi =Ml−i ·Gi +Gl−i. (6)

3.5 Node Weight Embedding and MultipleChannels

As depicted in Figure 2, since different hyper-nodes may havedifferent node weights, we assume such consequent node

Algorithm 1: The graph coarsening operationInput: Graph Gi and node representation Hi

Output: Coarsened graph Gi+1 and node representationHi+1

1 Calculate GCN output Gi according to Eq. (1)2 Initialize all nodes as unmarked/* Structural equivalence grouping */

3 Group and mark node pairs having the same neighbors/* Structural similarity grouping */

4 Sort all unmarked nodes in ascending order according tothe number of neighbors

5 repeat6 for each unmarked node vj do7 for each unmark node vk adjacent to vj do8 Calculate s(vj , vk) according to Eq. (2)9 Group and mark the node pair (vj , vk) having the

largest s(vj , vk)

10 until all nodes are marked11 Update node weights and edge weights12 Construct grouping matrix Mi according to Eq. (3)13 Calculate node representation Hi+1 according to Eq. (4)14 Construct coarsened graph Gi+1 according to Eq. (5)15 return Gi+1, Hi+1

weights could reflect the hierarchical characteristics of coars-ened graphs. In order to better capture the hierarchical in-formation, we use the node weight embedding to supplementinformation in Hi. Here we transform the node weight intoreal-valued vectors by looking up one randomly initializednode weight embedding matrix V ∈ R|T |×p, where T is theset of node weights and p is the dimension of the embed-ding. We apply node weight embedding in every coarseningand refining layer. For graph Gi, we obtain its node weightembedding Si ∈ Rni×p by looking up V according to thenode weight. For example, if one hyper-node contains threenodes, the third row of V will be selected as its node weightembedding. We then concatenate Hi and Si and the result-ing (d+p)-dimensional matrix will be fed into the next GCNlayer subsequently.

Multi-channel mechanisms help explore features in dif-ferent subspaces and H-GCN employs multiple channels onGCN to obtain rich information jointly at each layer. Afterobtained c channels

[G1

i , G2i , . . . , G

ci

], we perform weighted

average on these feature maps:

Gi =

c∑

j=1

wj ·Gji , (7)

where wj is a trainable weight of channel j.

3.6 The Output LayerFinally, in the output layer l, we use a GCN with a softmaxclassifier on Hl−1 to output probabilities of nodes:

Hl = softmax(ReLU

(D− 1

2

l AlD− 1

2

l Hl−1θl))

, (8)

where θl ∈ Rd×|Y| is a trainable weight matrix and Hl ∈Rn1×|Y| denotes the probabilities of nodes belonging to eachclass y ∈ Y .

The loss function is defined as the cross-entropy of predic-tions over the labeled nodes:

L = −m∑

i=1

|Y|∑

y=1

I(hi = yi) logP (hi, yi), (9)

where I(·) is the indicator function, yi is the true label for vi,hi is the prediction for labeled node vi, and P (hi, yi) is thepredicted probability that vi is of class yi.

3.7 Complexity Analysis and Model ComparisonIn this section, we analyze the model complexity and com-pare it with mainstream graph convolutional models, such asGCN and GAT.

For GCN, preprocessing matrices D− 1

2i AiD

− 12

i takesO(n3), and the training process for each layer takesO(|E|CF ), where E is the edge set and C,F are embeddingdimensions. For GAT, the masked attention over all nodesrequires O(n2) in the training process.

For H-GCN, the preprocessing takes O(n log n) to sort theunmarked nodes and O(mn) for SSG, where m is the aver-age number of neighborhoods. For training, the complexityis also O(|E|CF ). Therefore, H-GCN is as asymptoticallyefficient as GCN and is more efficient than GAT.

4 Experiments and Analysis4.1 Experimental SettingsDatasetsFor a comprehensive comparison with state-of-the-art meth-ods, we use four widely-used datasets including three cita-tion networks and one knowledge graph. We conduct semi-supervised node classification task in the transductive setting.The statistics of these datasets are summarized in Table 1. Weset the node weight and edge weight of the graph to one for allfour datasets. The dataset configuration follows the same set-ting in [Yang et al., 2016; Kipf and Welling, 2017] for a faircomparison. For citation networks, documents and citationsare treated as nodes and edges, respectively. For the knowl-edge graph, each triplet (e1, r, e2) will be assigned with sep-arate relation nodes r1 and r2 as (e1, r1) and (e2, r2), wheree1 and e2 are entities and r is the relation between them. Dur-ing training, only 20 labels per class are used for each citationnetwork and only one label per class is used for NELL duringtraining. Besides, 500 nodes in each dataset are selected ran-domly as the validation set. We do not use the labels of thevalidation set for model training.

Baseline MethodsTo evaluate the performance of H-GCN, we compare ourmethod with the following representative methods:

• DeepWalk [Perozzi et al., 2014] generates the node em-bedding via random walks in an unsupervised manner,then nodes are classified by feeding the embedding vec-tors into an SVM classifier.

Dataset Cora Citeseer Pubmed NELL

Type Citation network Knowledge graph# Vertices 2,708 3,327 19,717 65,755# Edges 5,429 4,732 44,338 266,144# Classes 7 6 3 210# Features 1,433 3,703 500 5,414Labeling rate 0.052 0.036 0.003 0.003

Table 1: Statistics of datasets used in experiments

Method Cora Citeseer Pubmed NELL

DeepWalk 67.2% 43.2% 65.3% 58.1%Planetoid 75.7% 64.7% 77.2% 61.9%

GCN 81.5% 70.3% 79.0% 73.0%GAT 83.0 ± 0.7% 72.5 ± 0.7% 79.0 ± 0.3% –

DGCN 83.5% 72.6% 79.3% 74.2%H-GCN 84.5 ± 0.5% 72.8 ± 0.5% 79.8 ± 0.4% 80.1 ± 0.4%

Table 2: Results of node classification in terms of accuracy

• Planetoid [Yang et al., 2016] not only learns node em-bedding but also predicts the context in graph. It alsoleverages label information to build both transductiveand inductive formulations.

• GCN [Kipf and Welling, 2017] produces node embed-ding vectors by truncating the Chebyshev polynomial tothe first-order neighborhoods.

• GAT [Velickovic et al., 2018] generates node embed-ding vectors by modeling the differences between thenode and its one-hop neighbors.

• DGCN [Zhuang and Ma, 2018] utilizes the graph adja-cency matrix and the positive mutual information matrixto encode both local consistency and global consistency.

Parameter SettingsWe train our model using Adam optimizer with a learningrate of 0.03 for 250 epochs. The dropout is applied to allfeature vectors with rates of 0.85. Besides, the `2 regular-ization factor is set to 0.0007. Considering different scalesof datasets, we set the total number of layers l to 9 for ci-tation networks and 11 for the knowledge graph, and applyfour-channel GCNs in both coarsening and refining layers.

4.2 Node Classification ResultsTo demonstrate the overall performance of semi-supervisednode classification, we compare the proposed method withother state-of-the-art methods. The performance in termsof accuracy is shown in Table 2. The best performance ofeach column is highlighted in boldface. The performance ofour proposed method is reported based on the average of 20measurements. Note that running GAT on the NELL datasetrequires more than 64G memory; hence its performance onNELL is not reported.

The results show that the proposed method consistentlyoutperforms other state-of-the-art methods, which verify theeffectiveness of the proposed coarsening and refining mecha-nisms. Notably, compared with citation networks, H-GCNsurpasses other baselines by larger margins on the NELLdataset. To be specific, the accuracy of H-GCN exceeds GCN

Method 20 15 10 5

GCN 79.0% 76.9% 72.2% 69.0%GAT 79.0% 77.3% 75.4% 70.3%

DGCN 79.3% 77.4% 76.7% 70.1%H-GCN 79.8% 79.3% 78.6% 76.5%

Table 3: Results of node classification in terms of accuracy onPubmed with labeled vertices varying from 20 per class to 5.

and DGCN by 7.1% and 5.9% on NELL dataset respectively.We analyze the results as follows.

Regarding traditional random-walk-based algorithms suchas DeepWalk and Planetoid, their performance is relativelypoor. DeepWalk cannot model the attribute information,which heavily restricts its performance. Though Planetoidcombines supervised information with an unsupervised loss,there is information loss of graph structure during randomsampling. To avoid that problem, GCN and GAT employthe neighborhood aggregation scheme to boost performance.GAT outperforms GCN as it can model different relationsto different neighbors rather than with a pre-defined order.DGCN further jointly models both local and global consis-tency, yet its global consistency is still obtained through ran-dom walks. As a result, the information in the graph structuremight lose in DGCN as well. On the contrary, the proposedH-GCN manages to capture global information through dif-ferent levels of convolutional layers and achieves the best re-sults among all four datasets.

Besides, on the NELL dataset, there are fewer trainingsamples per class than in citation networks. Under suchcircumstance, training nodes are further away from testingnodes on average. The baseline models with the restrictedreceptive field are unable to propagate the features and thesupervised information of the training nodes to other nodessufficiently. As a result, the proposed H-GCN with increasedreceptive fields and deeper layers obtains more promising im-provements than baselines.

4.3 Impact of Scale of Training DataWe suppose that a larger receptive field in the convolutionalmodel promotes the propagation of features and labels ongraphs. To verify the proposed H-GCN can get a larger re-ceptive field, we reduce the number of training samples tocheck if H-GCN still performs well when limited labeled datais given. As in nature, there are plenty of unlabeled data; it isalso of great significance to train the model with limited la-beled data. In this section, we conduct experiments with dif-ferent numbers of labeled instances on the Pubmed dataset.We vary the number of labeled nodes from 20 to 5 per class,where the labeled data is randomly chosen from the originaltraining set. All parameters are the same as previously de-scribed. The corresponding performance in terms of accuracyis reported in Table 3.

From the table, it can be observed that our method outper-form other baselines in all cases. With the number of labeleddata decreasing, our method obtains a more considerable mar-gin over these baseline algorithms. Especially when only fivelabeled nodes per class (≈ 0.08% labeling rate) are given,

Method Cora Citeseer Pubmed NELL

H-GCN without coarsen-ing and refining layers 80.3% 70.5% 76.8% 75.9%

H-GCN without nodeweight embeddings 84.2% 72.4% 79.5% 79.6%

H-GCN 84.5% 72.8% 79.8% 80.1%

Table 4: Results of the ablation study

the accuracy of H-GCN exceeds GCN, DGCN, and GAT by7.5%, 6.4%, and 6.2% respectively. When the number oftraining data decreases, it is more likely for an unlabeled nodeto be further away from these labeled nodes. Only when thereceptive field is large enough can information from thosetraining nodes be captured. As the receptive field of GCNand GAT does not exceed 2-hop neighborhoods, supervisedinformation contained in the training nodes cannot propagatesufficiently to other nodes. Therefore, these baselines down-grade considerably. However, owing to its larger receptivefield, the performance of H-GCN declines slightly when la-beled data decreases dramatically. Overall, it is verified thatthe proposed H-GCN with increased receptive fields is well-suited when training data is extremely scarce and thereby isof significant practical values.

4.4 Ablation StudyTo verify the effectiveness of the proposed coarsening andrefining layers, we conduct ablation study on coarsening andrefining layers and node weight embeddings respectively inthis section. The results are shown in Table 4.

Coarsening and refining layers. We remove all coarsen-ing and refining operations of H-GCN and compare its per-formance with the original H-GCN. Different from simplyadding too many GCN layers, we preserve the short-cut con-nection between the symmetric layers in the ablation study.From the results, it is evident that the proposed H-GCN hasbetter performance compared to H-GCN without coarseningmechanisms on all datasets. It can be verified that the coars-ening and refining mechanisms contribute to the performanceimprovements since they can obtain global information withlarger receptive fields.

Node weight embeddings. To study the impact of nodeweight embeddings, we compare H-GCN with no nodeweight embeddings used. It can be seen from results that themodel with node weight embeddings performs better, whichverifies the necessity to add this embedding vector in the nodeembeddings.

4.5 Sensitivity AnalysisLast, we analyze hyper-parameter sensitivity. Specifically, weinvestigate how different numbers of coarsening layers anddifferent numbers of channels will affect the results respec-tively. The performance is reported in terms of accuracy onall four datasets. While one parameter studied in the sensi-tivity analysis is changed, other hyper-parameters remain thesame.

1 2 3 4 5 6 7 850.0%

60.0%

70.0%

80.0%

CoraCiteseerPubmedNELL

(a) Coarsening layers

1 2 3 4 5 6 7 850.0%

60.0%

70.0%

80.0%

CoraCiteseerPubmedNELL

(b) Channel numbers

Figure 3: Results of H-GCN with varying layers and channels interms of accuracy.

Effects of coarsening layers. Since the coarsening layersin our model control the granularity of the receptive fieldenlargement, we experiment with one to eight coarseningand symmetric refining layers, where the results are shownin Figure 3(a). It is seen that the performance of H-GCNachieves the best when there are four coarsening layers onthree citation networks and five on the knowledge graph. Itis suspected that, since less labeled nodes are supplied onNELL than others, deeper layers and larger receptive fieldsare needed. However, when adding too many coarsening lay-ers, the performance drops due to overfitting.

Effects of channel numbers. Next, we investigate the im-pact of different amounts of channels on the performance.Multiple channels benefit the graph convolutional networkmodel, since they explore different feature subspaces, asshown in Figure 3(b). From the figure, it can be found that theperformance improves with the number of channels increas-ing until four channels, which demonstrates that more chan-nels help capture accurate node features. Nevertheless, toomany channels will inevitably introduce redundant parame-ters to the model, leading to overfitting as well.

5 ConclusionIn this paper, we have proposed a novel hierarchical graphconvolutional networks for the semi-supervised node classi-fication task. The H-GCN model consists of coarsening lay-ers and symmetric refining layers. By grouping structurallysimilar nodes to hyper-nodes, our model can get a larger re-ceptive field and enable sufficient information propagation.Compared with other previous work, our proposed H-GCNis deeper and can fully utilize both local and global infor-mation. Comprehensive experiments have confirmed that theproposed method consistently outperformed other state-of-the-art methods. In particular, our method has achieved sub-stantial gains over them in the case that labeled data is ex-tremely scarce.

AcknowledgementsThis work is jointly supported by National Natural Sci-ence Foundation of China (61772528) and National KeyResearch and Development Program (2016YFB1001000,2018YFB1402600).

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