Graphs have been widely adopted to accomplish fraud detection tasks because of their
inherently favorable structure to capture the intricate features in many complicated …

The rapid spread of infectious diseases and online rumors share similarities in terms of their
speed, scale, and patterns of contagion. Although these two phenomena have historically …

Centrality is a family of metrics for characterizing the importance of a vertex in a graph.
Although a large number of centrality metrics have been proposed, a majority of them …

Eigenvector embeddings have been widely used to study graph properties in signal
processing, mining, and learning tasks. However, if a graph is changing dynamically, these …

Intrusion detection systems have been proposed for the detection of botnet attacks. Various
types of centralized or distributed cloud-based machine learning and deep learning models …

Influence maximization is one of the most important topics in the social network analysis
field. As all the social networks can be considered signed, explicitly or implicitly, assessing …

Hub genes play an essential role in biological systems because of their interaction with
other genes. A vocabulary used in bioinformatics called Gene Ontology (GO) describes how …

With the increasing need for graph analysis, massive Concurrent iterative Graph Processing
(CGP) jobs are usually performed on the common large-scale real-world graph. Although …

The COVID-19 pandemic brought not only global devastation but also an unprecedented
infodemic of false or misleading information that spread rapidly through online social …

This paper presents ZePoP, a leader election protocol for distributed systems, optimizing a
delay-based closeness centrality. We design the protocol specifically for the Peer to Peer …