Block Modeling-Guided Graph Convolutional Neural Networks
- URL: http://arxiv.org/abs/2112.13507v2
- Date: Tue, 28 Dec 2021 02:27:29 GMT
- Title: Block Modeling-Guided Graph Convolutional Neural Networks
- Authors: Dongxiao He and Chundong Liang and Huixin Liu and Mingxiang Wen and
Pengfei Jiao and Zhiyong Feng
- Abstract summary: Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation.
We introduce block modeling into the framework of GCN so that it can realize "block-guided classified aggregation"
GCN is able to aggregate information from homophilic and heterophilic neighbors discriminately according to their homophily degree.
- Score: 17.39859951491802
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Graph Convolutional Network (GCN) has shown remarkable potential of exploring
graph representation. However, the GCN aggregating mechanism fails to
generalize to networks with heterophily where most nodes have neighbors from
different classes, which commonly exists in real-world networks. In order to
make the propagation and aggregation mechanism of GCN suitable for both
homophily and heterophily (or even their mixture), we introduce block modeling
into the framework of GCN so that it can realize "block-guided classified
aggregation", and automatically learn the corresponding aggregation rules for
neighbors of different classes. By incorporating block modeling into the
aggregation process, GCN is able to aggregate information from homophilic and
heterophilic neighbors discriminately according to their homophily degree. We
compared our algorithm with state-of-art methods which deal with the
heterophily problem. Empirical results demonstrate the superiority of our new
approach over existing methods in heterophilic datasets while maintaining a
competitive performance in homophilic datasets.
Related papers
- Refining Latent Homophilic Structures over Heterophilic Graphs for
Robust Graph Convolution Networks [23.61142321685077]
Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data.
Our study marks the pioneering attempt to quantitatively investigate the GCN robustness over omnipresent heterophilic graphs for node classification.
We present a novel method that aims to harden GCNs by automatically learning Latent Homophilic Structures over heterophilic graphs.
arXiv Detail & Related papers (2023-12-27T05:35:14Z) - Histopathology Whole Slide Image Analysis with Heterogeneous Graph
Representation Learning [78.49090351193269]
We propose a novel graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis.
Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic attribute similarity to each edge.
Our framework outperforms the state-of-the-art methods with considerable margins on various tasks.
arXiv Detail & Related papers (2023-07-09T14:43:40Z) - HomoGCL: Rethinking Homophily in Graph Contrastive Learning [64.85392028383164]
HomoGCL is a model-agnostic framework to expand the positive set using neighbor nodes with neighbor-specific significances.
We show that HomoGCL yields multiple state-of-the-art results across six public datasets.
arXiv Detail & Related papers (2023-06-16T04:06:52Z) - Heterophily-Aware Graph Attention Network [42.640057865981156]
Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning.
Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem.
We propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily.
arXiv Detail & Related papers (2023-02-07T03:21:55Z) - RAW-GNN: RAndom Walk Aggregation based Graph Neural Network [48.139599737263445]
We introduce a novel aggregation mechanism and develop a RAndom Walk Aggregation-based Graph Neural Network (called RAW-GNN) method.
The new method utilizes breadth-first random walk search to capture homophily information and depth-first search to collect heterophily information.
It replaces the conventional neighborhoods with path-based neighborhoods and introduces a new path-based aggregator based on Recurrent Neural Networks.
arXiv Detail & Related papers (2022-06-28T12:19:01Z) - Heterogeneous Graph Neural Networks using Self-supervised Reciprocally
Contrastive Learning [102.9138736545956]
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs.
We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies.
In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
arXiv Detail & Related papers (2022-04-30T12:57:02Z) - Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism
for Homophily and Heterophily [38.50800951799888]
Graph Convolutional Networks (GCNs) have been widely applied in various fields due to their significant power on processing graph-structured data.
Existing methods deal with heterophily by mainly aggregating higher-order neighborhoods or combing the immediate representations.
This paper proposes a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily.
arXiv Detail & Related papers (2021-12-27T08:19:23Z) - Attention-driven Graph Clustering Network [49.040136530379094]
We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN)
AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature.
AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
arXiv Detail & Related papers (2021-08-12T02:30:38Z) - Graph Neural Networks with Heterophily [40.23690407583509]
We propose a novel framework called CPGNN that generalizes GNNs for graphs with either homophily or heterophily.
We show that replacing the compatibility matrix in our framework with the identity (which represents pure homophily) reduces to GCN.
arXiv Detail & Related papers (2020-09-28T18:29:36Z) - Heterogeneous Graph Transformer [49.675064816860505]
Heterogeneous Graph Transformer (HGT) architecture for modeling Web-scale heterogeneous graphs.
To handle dynamic heterogeneous graphs, we introduce the relative temporal encoding technique into HGT.
To handle Web-scale graph data, we design the heterogeneous mini-batch graph sampling algorithm---HGSampling---for efficient and scalable training.
arXiv Detail & Related papers (2020-03-03T04:49:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.