PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities

• URL: http://arxiv.org/abs/2409.11618v1
• Date: Wed, 18 Sep 2024 00:49:42 GMT
• Title: PieClam: A Universal Graph Autoencoder Based on Overlapping Inclusive and Exclusive Communities
• Authors: Daniel Zilberg, Ron Levie,
• Abstract summary: PieClam is a graph autoencoder for representing any graph as overlapping generalized communities. We show that PieClam is a universal autoencoder, able to uniformly approximately reconstruct any graph.
• Score: 7.130067003076749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose PieClam (Prior Inclusive Exclusive Cluster Affiliation Model): a probabilistic graph model for representing any graph as overlapping generalized communities. Our method can be interpreted as a graph autoencoder: nodes are embedded into a code space by an algorithm that maximizes the log-likelihood of the decoded graph, given the input graph. PieClam is a community affiliation model that extends well-known methods like BigClam in two main manners. First, instead of the decoder being defined via pairwise interactions between the nodes in the code space, we also incorporate a learned prior on the distribution of nodes in the code space, turning our method into a graph generative model. Secondly, we generalize the notion of communities by allowing not only sets of nodes with strong connectivity, which we call inclusive communities, but also sets of nodes with strong disconnection, which we call exclusive communities. To model both types of communities, we propose a new type of decoder based the Lorentz inner product, which we prove to be much more expressive than standard decoders based on standard inner products or norm distances. By introducing a new graph similarity measure, that we call the log cut distance, we show that PieClam is a universal autoencoder, able to uniformly approximately reconstruct any graph. Our method is shown to obtain competitive performance in graph anomaly detection benchmarks.

Related papers

• GRAND : Graph Reconstruction from potential partial Adjacency and Neighborhood Data [4.893653298798788]
  cryptographic approaches, such as secure multiparty computation, can be used to compute in a secure manner the function of a distributed graph without centralizing the data of each participant. We propose an approach by which an adversary observing the result of a private protocol for the computation of the number of common neighbors can reconstruct the adjacency matrix of the graph. We consider two models of adversary, one who observes the common neighbors matrix only, and a knowledgeable one, that has a partial knowledge of the original graph.
  arXiv  Detail & Related papers  (2024-12-03T09:42:28Z)
• Gramformer: Learning Crowd Counting via Graph-Modulated Transformer [68.26599222077466]
  Gramformer is a graph-modulated transformer to enhance the network by adjusting the attention and input node features respectively. A feature-based encoding is proposed to discover the centrality positions or importance of nodes. Experiments on four challenging crowd counting datasets have validated the competitiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-01-08T13:01:54Z)
• Curvature-based Clustering on Graphs [14.136746708037402]
  We study algorithms, which exploit the geometry of the graph to identify densely connected substructures, which form clusters or communities. Our method implements discrete Ricci curvatures and their associated geometric flows, under which the edge weights of the graph evolve to reveal its community structure.
  arXiv  Detail & Related papers  (2023-07-19T17:35:08Z)
• HiGen: Hierarchical Graph Generative Networks [2.3931689873603603]
  Most real-world graphs exhibit a hierarchical structure, which is often overlooked by existing graph generation methods. We propose a novel graph generative network that captures the hierarchical nature of graphs and successively generates the graph sub-structures in a coarse-to-fine fashion. This modular approach enables scalable graph generation for large and complex graphs.
  arXiv  Detail & Related papers  (2023-05-30T18:04:12Z)
• Fair Community Detection and Structure Learning in Heterogeneous Graphical Models [8.643517734716607]
  Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. This paper defines a novel $ell_$-regularized pseudo-likelihood approach for fair graphical model selection.
  arXiv  Detail & Related papers  (2021-12-09T18:58:36Z)
• Graph Communal Contrastive Learning [34.85906025283825]
  A fundamental problem for graph representation learning is how to effectively learn representations without human labeling. We propose a novel Graph Contrastive Learning (gCooL) framework to jointly learn the community partition and learn node representations.
  arXiv  Detail & Related papers  (2021-10-28T02:57:54Z)
• 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)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Factorizable Graph Convolutional Networks [90.59836684458905]
  We introduce a novel graph convolutional network (GCN) that explicitly disentangles intertwined relations encoded in a graph. FactorGCN takes a simple graph as input, and disentangles it into several factorized graphs. We evaluate the proposed FactorGCN both qualitatively and quantitatively on the synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2020-10-12T03:01:40Z)
• Modeling Graph Structure via Relative Position for Text Generation from Knowledge Graphs [54.176285420428776]
  We present Graformer, a novel Transformer-based encoder-decoder architecture for graph-to-text generation. With our novel graph self-attention, the encoding of a node relies on all nodes in the input graph - not only direct neighbors - facilitating the detection of global patterns. Graformer learns to weight these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph.
  arXiv  Detail & Related papers  (2020-06-16T15:20:04Z)
• Auto-decoding Graphs [91.3755431537592]
  The generative model is an auto-decoder that learns to synthesize graphs from latent codes. Graphs are synthesized using self-attention modules that are trained to identify likely connectivity patterns.
  arXiv  Detail & Related papers  (2020-06-04T14:23:01Z)

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.