Citation: Liu, J.; Zhong, Y.
Time-Weighted Community Search
Based on Interest. Appl. Sci. 2022, 12,
7077. https://doi.org/10.3390/
app12147077
Academic Editors: Sławomir
Nowaczyk, Rita P. Ribeiro and
Grzegorz Nalepa
Received: 7 May 2022
Accepted: 11 July 2022
Published: 13 July 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Time-Weighted Community Search Based on Interest
Jing Liu
1,2
and Yong Zhong
1,2,
*
1
Chengdu Institute of Computer Applications, Chinese Academy of Sciences, Chengdu 610041, China;
liujing186@mails.ucas.ac.cn
2
School of Computer Science and Technology, University of Chinese Academy of Sciences,
Beijing 100049, China
* Correspondence: zhongyong@casit.com.cn
Abstract:
Community search aims to provide users with personalized community query services.
It is a prerequisite for various recommendation systems and has received widespread attention
from academia and industry. The existing literature has established various community search
models and algorithms from different dimensions of social networks. Unfortunately, they only judge
the representative attributes of users according to the frequency of attribute keywords, completely
ignoring the temporal characteristics of keywords. It is clear that a user’s interest changes over
time, so it is essential to select users’ representative attributes in combination with time. Therefore,
we propose a time-weighted community search model (TWC) based on user interests which fully
considers the impact of time on user interests. TWC reduces the number of query parameters as much
as possible and improves the usability of the model. We design the time-weighted decay function
of the attribute. We then extract the user’s time-weighted representative attributes to express the
user’s short-term interests more clearly in the query window. In addition, we propose a new attribute
similarity scoring function and a community scoring function. To solve the TWC problem, we design
and implement the Local Extend algorithm and the Shrink algorithm. Finally, we conduct extensive
experiments on a real dataset to verify the superiority of the TWC model and the efficiency of the
proposed algorithm.
Keywords:
community search; attributed network; short-term interests; time-weighted score function
1. Introduction
Community is the basis for various recommendation applications, such as friend
recommendation, precision marketing, and activity organization [
1
–
4
]. Compared with
community detection, community search can better explain the reasons for the formation of
the community by personalized search according to the query criteria specified by the user.
Therefore, community search has attracted extensive attention in academia and industry.
Many query models and algorithms have emerged to serve various query scenarios.
Unfortunately, the existing models [
2
–
5
] do not fully describe users, especially their
representative attributes (including text and spatial attributes). Take the geographic social
network Foursquare as an example: it consists of millions of nodes, where each node
represents a user, and an edge represents the friendship between two users. Users check
in at different times and places. To make the query closer to reality, we should describe
users based on multiple dimensions, such as spatial distance, social relations, interest
attributes, and time. However, these models only judge the representative attributes of
users according to the frequency of keywords. It is worth emphasizing that time plays
an extremely important role in establishing social networks. The time change of users’
check-in records can best reflect their real interest trends. Especially in the recommendation
system, we are more concerned with the short-term interests of users. The more recent the
attributes are, the stronger the ability to represent users’ current interests, and the greater
Appl. Sci. 2022, 12, 7077. https://doi.org/10.3390/app12147077 https://www.mdpi.com/journal/applsci
