Random walks are ubiquitous in the sciences, and they are interesting from both theoretical
and practical perspectives. They are one of the most fundamental types of stochastic …

With the rapid development of information technologies, various big graphs are prevalent in
many real applications (eg, social media and knowledge bases). An important component of …

The use of big data research methods has grown tremendously over the past five years in
both academia and industry. As the size and complexity of available datasets has grown, so …

Community detection is an important task in network analysis. A community (also referred to
as a cluster) is a set of cohesive vertices that have more connections inside the set than …

Given a large network, local community detection aims at finding the community that
contains a set of query nodes and also maximizes (minimizes) a goodness metric. This …

To design effective vaccine policies, policymakers need detailed data about who has been
vaccinated, who is holding out, and why. However, existing data in the US are insufficient …

Large graphs arise in a number of contexts and understanding their structure and extracting
information from them is an important research area. Early algorithms on mining …

Large graphs arise in a number of contexts and understanding their structure and extracting
information from them is an important research area. Early algorithms for mining …

Given a network, local community detection (aka graph clustering) methods aim at finding
communities around the selected initial nodes (also referred to as seeds, starting nodes or …

This paper introduces a new graph-theoretical concept of hidden community for analysing
complex networks, which contain both stronger or dominant communities and weak …