A comprehensive survey on community detection with deep learning
Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …
connections of a group of members that are different from those in other communities. The …
A survey of community detection approaches: From statistical modeling to deep learning
Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …
into multiple sub-structures to help reveal their latent functions. Community detection has …
Connect, not collapse: Explaining contrastive learning for unsupervised domain adaptation
We consider unsupervised domain adaptation (UDA), where labeled data from a source
domain (eg, photos) and unlabeled data from a target domain (eg, sketches) are used to …
domain (eg, photos) and unlabeled data from a target domain (eg, sketches) are used to …
Efficient and degree-guided graph generation via discrete diffusion modeling
Diffusion-based generative graph models have been proven effective in generating high-
quality small graphs. However, they need to be more scalable for generating large graphs …
quality small graphs. However, they need to be more scalable for generating large graphs …
A neural collapse perspective on feature evolution in graph neural networks
Graph neural networks (GNNs) have become increasingly popular for classification tasks on
graph-structured data. Yet, the interplay between graph topology and feature evolution in …
graph-structured data. Yet, the interplay between graph topology and feature evolution in …
A survey on Bayesian deep learning
A comprehensive artificial intelligence system needs to not only perceive the environment
with different “senses”(eg, seeing and hearing) but also infer the world's conditional (or even …
with different “senses”(eg, seeing and hearing) but also infer the world's conditional (or even …
Understanding non-linearity in graph neural networks from the bayesian-inference perspective
Graph neural networks (GNNs) have shown superiority in many prediction tasks over graphs
due to their impressive capability of capturing nonlinear relations in graph-structured data …
due to their impressive capability of capturing nonlinear relations in graph-structured data …
Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization
Recently there has been increased interest in semi-supervised classification in the presence
of graphical information. A new class of learning models has emerged that relies, at its most …
of graphical information. A new class of learning models has emerged that relies, at its most …
Effects of graph convolutions in multi-layer networks
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are
used to solve classification problems accompanied by graphical information. We present a …
used to solve classification problems accompanied by graphical information. We present a …
Fitting autoregressive graph generative models through maximum likelihood estimation
We consider the problem of fitting autoregressive graph generative models via maximum
likelihood estimation (MLE). MLE is intractable for graph autoregressive models because the …
likelihood estimation (MLE). MLE is intractable for graph autoregressive models because the …