37 papers with code ·
Graphs

**Graph Clustering** is the process of grouping the nodes of the graph into clusters, taking into account the edge structure of the graph in such a way that there are several edges within each cluster and very few between clusters. Graph Clustering intends to partition the nodes in the graph into disjoint groups.

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE).

Ranked #2 on Link Prediction on Pubmed (Accuracy metric)

Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.

COMMUNITY DETECTION GRAPH CLUSTERING NETWORK EMBEDDING NODE CLASSIFICATION

Unlike NMF, however, SymNMF is based on a similarity measure between data points, and factorizes a symmetric matrix containing pairwise similarity values (not necessarily nonnegative).

This work considers the problem of computing distances between structured objects such as undirected graphs, seen as probability distributions in a specific metric space.

Ranked #2 on Graph Classification on ENZYMES

Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].

Ranked #1 on Node Classification on Amazon2M

We have tested the effectiveness of GRAPH-BERT on several graph benchmark datasets.

Ranked #13 on Node Classification on Cora

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.

Ranked #2 on Link Prediction on Pubmed

ADVERSARIAL TRAINING GRAPH CLUSTERING GRAPH EMBEDDING LINK PREDICTION

To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically.

In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks.

Local graph clustering and the closely related seed set expansion problem are primitives on graphs that are central to a wide range of analytic and learning tasks such as local clustering, community detection, nodes ranking and feature inference.