A survey on graph counterfactual explanations: definitions, methods, evaluation, and research challenges
Graph Neural Networks (GNNs) perform well in community detection and molecule
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …
classification. Counterfactual Explanations (CE) provide counter-examples to overcome the …
[HTML][HTML] A novel nonnegative matrix factorization-based model for attributed graph clustering by incorporating complementary information
Attributed graph clustering is a prominent research area, catering to the increasing need for
understanding real-world systems by uncovering exhaustive meaningful latent knowledge …
understanding real-world systems by uncovering exhaustive meaningful latent knowledge …
A review on community detection in large complex networks from conventional to deep learning methods: A call for the use of parallel meta-heuristic algorithms
Complex networks (CNs) have gained much attention in recent years due to their
importance and popularity. The rapid growth in the size of CNs leads to more difficulties in …
importance and popularity. The rapid growth in the size of CNs leads to more difficulties in …
Graph regularized nonnegative matrix factorization for community detection in attributed networks
Community detection has become an important research topic in machine learning due to
the proliferation of network data. However, most existing methods have been developed …
the proliferation of network data. However, most existing methods have been developed …
Self-supervised temporal graph learning with temporal and structural intensity alignment
Temporal graph learning aims to generate high-quality representations for graph-based
tasks with dynamic information, which has recently garnered increasing attention. In contrast …
tasks with dynamic information, which has recently garnered increasing attention. In contrast …
WSNMF: Weighted symmetric nonnegative matrix factorization for attributed graph clustering
Abstract In recent times, Symmetric Nonnegative Matrix Factorization (SNMF), a derivative of
Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph …
Nonnegative Matrix Factorization (NMF), has surfaced as a promising technique for graph …
Obfuscating community structure in complex network with evolutionary divide-and-conquer strategy
As the number of social network users grows exponentially with increasingly complex
profiles, community detection algorithms play a critical role in user portrait analysis. The …
profiles, community detection algorithms play a critical role in user portrait analysis. The …
DAC-HPP: deep attributed clustering with high-order proximity preserve
Attributed graph clustering, the task of grou** nodes into communities using both graph
structure and node attributes, is a fundamental problem in graph analysis. Recent …
structure and node attributes, is a fundamental problem in graph analysis. Recent …
A learning convolutional neural network approach for network robustness prediction
Network robustness is critical for various societal and industrial networks against malicious
attacks. In particular, connectivity robustness and controllability robustness reflect how well a …
attacks. In particular, connectivity robustness and controllability robustness reflect how well a …
A self-adaptive evolutionary deception framework for community structure
The rapid development of community detection algorithms, while serving users in social
networks, also brings about certain privacy problems. In this work, we study community …
networks, also brings about certain privacy problems. In this work, we study community …