4 research outputs found
Deep Learning in Social Networks for Overlappering Community Detection
The collection of nodes is termed as community in any network system that are tightly associated to the other nodes. In network investigation, identifying the community structure is crucial task, particularly for exposing connections between certain nodes. For community overlapping, network discovery, there are numerous methodologies described in the literature. Numerous scholars have recently focused on network embedding and feature learning techniques for node clustering. These techniques translate the network into a representation space with fewer dimensions. In this paper, a deep neural network-based model for learning graph representation and stacked auto-encoders are given a nonlinear embedding of the original graph to learn the model. In order to extract overlapping communities, an AEOCDSN algorithm is used. The efficiency of the suggested model is examined through experiments on real-world datasets of various sizes and accepted standards. The method outperforms various well-known community detection techniques, according to empirical findings
Deep Adaptive Graph Clustering via Von Mises-Fisher Distributions
Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed Deep Adaptive Graph Clustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings H can be drawn from a von Mises-Fisher distribution and each cluster k is associated with cluster inherent parameters ρk which includes cluster center μ and cluster cohesion degree κ. Then we adopt an EM-like approach (i.e., (H|ρ) and (ρ|H), respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets
Weighted Flow Diffusion for Local Graph Clustering with Node Attributes: an Algorithm and Statistical Guarantees
Local graph clustering methods aim to detect small clusters in very large
graphs without the need to process the whole graph. They are fundamental and
scalable tools for a wide range of tasks such as local community detection,
node ranking and node embedding. While prior work on local graph clustering
mainly focuses on graphs without node attributes, modern real-world graph
datasets typically come with node attributes that provide valuable additional
information. We present a simple local graph clustering algorithm for graphs
with node attributes, based on the idea of diffusing mass locally in the graph
while accounting for both structural and attribute proximities. Using
high-dimensional concentration results, we provide statistical guarantees on
the performance of the algorithm for the recovery of a target cluster with a
single seed node. We give conditions under which a target cluster generated
from a fairly general contextual random graph model, which includes both the
stochastic block model and the planted cluster model as special cases, can be
fully recovered with bounded false positives. Empirically, we validate all
theoretical claims using synthetic data, and we show that incorporating node
attributes leads to superior local clustering performances using real-world
graph datasets.Comment: 30 pages, 2 figures, 9 table