22 papers with code ·
Graphs

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

#2 best model for Graph Clustering on Citeseer

KDD 2019 • benedekrozemberczki/ClusterGCN •

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].

SOTA for Node Classification on Pubmed (F1 metric )

KDD 2019 • benedekrozemberczki/ClusterGCN •

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].

SOTA for Node Classification on Amazon2M

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

SOTA for Graph Clustering on Cora

ICLR 2018 • xbresson/spatial_graph_convnets •

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.

cnmusco/graph-similarity-learning

•In this work we consider a privacy threat to a social network in which an attacker has access to a subset of random walk-based node similarities, such as effective resistances (i. e., commute times) or personalized PageRank scores.

NeurIPS 2017 • MahsaDerakhshan/AffinityClustering

In particular, we propose affinity, a novel hierarchical clustering based on Boruvka's MST algorithm.

We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains.

We present a novel hierarchical graph clustering algorithm inspired by modularity-based clustering techniques.

We introduce a novel algorithm to perform graph clustering in the edge streaming setting.