Anomaly detection in online social networks

Anomaly detection in online social networks

Anomalies in online social networks can signify irregular, and often illegal behaviour. Detection of such anomalies has been used to identify malicious individuals, including spammers, sexual predators, and online fraudsters. In this paper we survey existing computational techniques for detecting anomalies in online social networks. We characterise anomalies as being either static or dynamic, a...

Analyzing the Effectiveness of Graph Metrics for Anomaly Detection in Online Social Networks

Online social networks can be modelled as graphs; in this paper, we analyze the use of graph metrics for identifying users with anomalous relationships to other users. A framework is proposed for analyzing the effectiveness of various graph theoretic properties such as the number of neighbouring nodes and edges, betweenness centrality, and community cohesiveness in detecting anomalous users. Ex...

Anomaly Detection in Online Social Networks: Using Data- Mining Techniques and Fuzzy Logic

Interpersonal Trust in Online Scientific Social Networks: Causes and Results

Background and Aim: This study tends to investigate the reasons of interpersonal trust and the results of trust in online scientific social networks. Methods: The applied Research has been used cluster sampling to collect data. The study population consisted of Shiraz university and Persian Gulf university faculties. A sampling of 269 person was determined by Morgan table according to whole pop...

Social Network Analysis: Online Anomaly Detection and Graphical Model Selection

There is a demand for computational methods that can extract meaningful patterns from social networks in real time. However, these networks can be extremely large and volatile, and brute force algorithms for high-dimensional data analysis are intractable as high costs and poor runtime preclude many real-world applications. I present two online (sequentially updating) strategies that from learn ...