Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

Unlike the existing literatures that consider only discrete-time networks, this paper explores
the double effects of both discrete time and discrete spatial diffusions in a switching complex …

Incorporating knowledge graphs (KGs) into recommender systems (RS) has recently
attracted increasing attention. For large-scale KGs, due to limited labour supervision, noises …

As an important part of machine learning, clustering methods have been continuously paid
attention to. Current clustering methods divide data objects usually based on Euclidean …

Current graph clustering methods emphasize individual node and edge connections, while
ignoring higher-order organization at the level of motif. Recently, higher-order graph …

Due to the extensive existence of time series in various fields, more and more research on
time series data mining, especially time series clustering, has been done in recent years …

This article introduces a novel vulnerability measure, based on the structure of complex
network communities, to assess the significance and security of network communities …

Abstract Users utilize Location-Based Social Networks (LBSNs) to check into diverse venues
and share their experiences through ratings and comments. However, these platforms …

One of the crucial research areas in the analysis of complex social networks is the
identification of communities. Since community detection is an NP-complete problem …

Public bicycles are an indispensable part of green public transportation and are also a
convenient and economical manner for the general public. In operation management, it is …