Break the Wall Between Homophily and Heterophily for Graph
Representation Learning
- URL: http://arxiv.org/abs/2210.05382v1
- Date: Sat, 8 Oct 2022 19:37:03 GMT
- Title: Break the Wall Between Homophily and Heterophily for Graph
Representation Learning
- Authors: Xiao Liu, Lijun Zhang, Hui Guan
- Abstract summary: Homophily and heterophily are intrinsic properties of graphs that describe whether two linked nodes share similar properties.
This work identifies three graph features, including the ego node feature, the aggregated node feature, and the graph structure feature, that are essential for graph representation learning.
It proposes a new GNN model called OGNN that extracts all three graph features and adaptively fuses them to achieve generalizability across the whole spectrum of homophily.
- Score: 25.445073413243925
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Homophily and heterophily are intrinsic properties of graphs that describe
whether two linked nodes share similar properties. Although many Graph Neural
Network (GNN) models have been proposed, it remains unclear how to design a
model so that it can generalize well to the whole spectrum of homophily. This
work addresses the challenge by identifying three graph features, including the
ego node feature, the aggregated node feature, and the graph structure feature,
that are essential for graph representation learning. It further proposes a new
GNN model called OGNN (Omnipotent Graph Neural Network) that extracts all three
graph features and adaptively fuses them to achieve generalizability across the
whole spectrum of homophily. Extensive experiments on both synthetic and real
datasets demonstrate the superiority (average rank 1.56) of our OGNN compared
with state-of-the-art methods.
Related papers
- The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs [59.03660013787925]
We introduce the Heterophily Snowflake Hypothesis and provide an effective solution to guide and facilitate research on heterophilic graphs.
Our observations show that our framework acts as a versatile operator for diverse tasks.
It can be integrated into various GNN frameworks, boosting performance in-depth and offering an explainable approach to choosing the optimal network depth.
arXiv Detail & Related papers (2024-06-18T12:16:00Z) - Finding the Missing-half: Graph Complementary Learning for
Homophily-prone and Heterophily-prone Graphs [48.79929516665371]
Graphs with homophily-prone edges tend to connect nodes with the same class.
Heterophily-prone edges tend to build relationships between nodes with different classes.
Existing GNNs only take the original graph during training.
arXiv Detail & Related papers (2023-06-13T08:06:10Z) - Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN.
We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
arXiv Detail & Related papers (2023-05-18T07:27:18Z) - Beyond Homophily: Reconstructing Structure for Graph-agnostic Clustering [15.764819403555512]
It is impossible to first identify a graph as homophilic or heterophilic before a suitable GNN model can be found.
We propose a novel graph clustering method, which contains three key components: graph reconstruction, a mixed filter, and dual graph clustering network.
Our method dominates others on heterophilic graphs.
arXiv Detail & Related papers (2023-05-03T01:49:01Z) - GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton.
The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases.
In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
arXiv Detail & Related papers (2022-12-01T11:49:07Z) - ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting [32.69196871253339]
We propose a novel Edge Splitting GNN (ES-GNN) framework to adaptively distinguish between graph edges either relevant or irrelevant to learning tasks.
We show that our ES-GNN can be regarded as a solution to a disentangled graph denoising problem.
arXiv Detail & Related papers (2022-05-27T01:29:03Z) - 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) - Incorporating Heterophily into Graph Neural Networks for Graph Classification [6.709862924279403]
Graph Neural Networks (GNNs) often assume strong homophily for graph classification, seldom considering heterophily.
We develop a novel GNN architecture called IHGNN (short for Incorporating Heterophily into Graph Neural Networks)
We empirically validate IHGNN on various graph datasets and demonstrate that it outperforms the state-of-the-art GNNs for graph classification.
arXiv Detail & Related papers (2022-03-15T06:48:35Z) - GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both
Homophily and Heterophily [24.742449127169586]
Graph Neural Networks (GNNs) are widely used on a variety of graph-based machine learning tasks.
For node-level tasks, GNNs have strong power to model the homophily property of graphs.
We propose a novel GNN model based on a bi- kernel feature transformation and a selection gate.
arXiv Detail & Related papers (2021-10-29T13:44:09Z) - Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised
Node Classification [59.06717774425588]
We propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.
It contains explicit pairwise factors to model output-output relations and uses a GNN backbone to model input-output relations.
We conduct experiments on various datasets, which shows that our model can effectively improve the performance for semi-supervised node classification on graphs.
arXiv Detail & Related papers (2021-07-27T19:47:53Z) - Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs [6.018995094882323]
Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs.
Most GNNs assume local homophily, i.e., strong similarities in localneighborhoods.
We propose a flexible GNN model, which is capable of handling any graphs without beingrestricted by their underlying homophily.
arXiv Detail & Related papers (2021-03-26T00:35:36Z)
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.