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Graph Clustering

10 papers with code · Graphs

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Residual Gated Graph ConvNets

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. We review existing graph RNN and ConvNet architectures, and propose natural extension of LSTM and ConvNet to graphs with arbitrary size.

GRAPH CLASSIFICATION GRAPH CLUSTERING

Adversarially Regularized Graph Autoencoder for Graph Embedding

13 Feb 2018Ruiqi-Hu/ARGA

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data.

GRAPH CLUSTERING GRAPH EMBEDDING LINK PREDICTION

Learning Networks from Random Walk-Based Node Similarities

23 Jan 2018cnmusco/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. We also show that it is possible to learn a graph which accurately matches the underlying network on all other effective resistances.

ANOMALY DETECTION GRAPH CLUSTERING GRAPH SIMILARITY

Hierarchical Graph Clustering using Node Pair Sampling

5 Jun 2018tbonald/paris

We present a novel hierarchical graph clustering algorithm inspired by modularity-based clustering techniques. The algorithm is agglomerative and based on a simple distance between clusters induced by the probability of sampling node pairs.

GRAPH CLUSTERING

A Streaming Algorithm for Graph Clustering

9 Dec 2017ahollocou/graph-streaming

We introduce a novel algorithm to perform graph clustering in the edge streaming setting. In this model, the graph is presented as a sequence of edges that can be processed strictly once.

GRAPH CLUSTERING

Non-linear Attributed Graph Clustering by Symmetric NMF with PU Learning

21 Sep 2018seijimaekawa/NAGC

We consider the clustering problem of attributed graphs. Our challenge is how we can design an effective and efficient clustering method that precisely captures the hidden relationship between the topology and the attributes in real-world graphs.

GRAPH CLUSTERING

A Projection Method for Metric-Constrained Optimization

5 Jun 2018nveldt/MetricOptimization

We outline a new approach for solving optimization problems which enforce triangle inequalities on output variables. We refer to this as metric-constrained optimization, and give several examples where problems of this form arise in machine learning applications and theoretical approximation algorithms for graph clustering.

GRAPH CLUSTERING

AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering

21 Sep 2016tgensol/AMOS

One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. Consequently, AMOS outputs clusters of minimal model order with statistical clustering reliability guarantees.

GRAPH CLUSTERING SPECTRAL GRAPH CLUSTERING

Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering

11 Apr 2016tgensol/AMOS

One of the longstanding open problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph.

GRAPH CLUSTERING SPECTRAL GRAPH CLUSTERING