scholarly journals Interpretable Probabilistic Divisive Clustering of Large Node-Attributed Networks

2017 ◽  
Author(s):  
Lisa Kaati ◽  
Adam Ruul
Keyword(s):  
Divisive Clustering ◽  
Attributed Networks

scholarly journals Jointly Learning Representations of Nodes and Attributes for Attributed Networks

2020 ◽  
Vol 38 (2) ◽  
pp. 1-32 ◽  
Author(s):  
Zaiqiao Meng ◽  
Shangsong Liang ◽  
Xiangliang Zhang ◽  
Richard McCreadie ◽  
Iadh Ounis
Keyword(s):  
Attributed Networks

Unifying community detection and network embedding in attributed networks

2021 ◽  
Vol 63 (5) ◽  
pp. 1221-1239
Author(s):  
Yu Ding ◽  
Hao Wei ◽  
Guyu Hu ◽  
Zhisong Pan ◽  
Shuaihui Wang
Keyword(s):  
Community Detection ◽  
Network Embedding ◽  
Attributed Networks

Cluster selection in divisive clustering algorithms

2002 ◽  
Author(s):  
Sergio M. Savaresi ◽  
Daniel L. Boley ◽  
Sergio Bittanti ◽  
Giovanna Gazzaniga
Keyword(s):  
Clustering Algorithms ◽  
Divisive Clustering

scholarly journals Differentially Private Attributed Network Releasing Based on Early Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yuye Wang ◽  
Jing Yang ◽  
Jianpei Zhan
Keyword(s):  
Topological Structure ◽  
Differential Privacy ◽  
Probability Model ◽  
Prior Work ◽  
Data Utility ◽  
High Data ◽  
Early Fusion ◽  
Attributed Network ◽  
Analysis Of Social Networks ◽  
Attributed Networks

Vertex attributes exert huge impacts on the analysis of social networks. Since the attributes are often sensitive, it is necessary to seek effective ways to protect the privacy of graphs with correlated attributes. Prior work has focused mainly on the graph topological structure and the attributes, respectively, and combining them together by defining the relevancy between them. However, these methods need to add noise to them, respectively, and they produce a large number of required noise and reduce the data utility. In this paper, we introduce an approach to release graphs with correlated attributes under differential privacy based on early fusion. We combine the graph topological structure and the attributes together with a private probability model and generate a synthetic network satisfying differential privacy. We conduct extensive experiments to demonstrate that our approach could meet the request of attributed networks and achieve high data utility.

scholarly journals Radar: Residual Analysis for Anomaly Detection in Attributed Networks

2017 ◽  
Author(s):  
Jundong Li ◽  
Harsh Dani ◽  
Xia Hu ◽  
Huan Liu
Keyword(s):  
Anomaly Detection ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Residual Analysis ◽  
Network Information ◽  
Learning Framework ◽  
Modeling Process ◽  
Data Representations ◽  
Gene Regulatory ◽  
Attributed Networks

Attributed networks are pervasive in different domains, ranging from social networks, gene regulatory networks to financial transaction networks. This kind of rich network representation presents challenges for anomaly detection due to the heterogeneity of two data representations. A vast majority of existing algorithms assume certain properties of anomalies are given a prior. Since various types of anomalies in real-world attributed networks co-exist, the assumption that priori knowledge regarding anomalies is available does not hold. In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection problems. However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process. Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection. By learning and analyzing the residuals, we detect anomalies whose behaviors are singularly different from the majority. Experiments on real datasets show the effectiveness and generality of the proposed framework.

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