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121 days to first decision for reviewed manuscripts only
115 days to first decision for all manuscripts
262 days from submission to acceptance
19 days from acceptance to publication
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In social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions are made by simply majority, which means that mos...
Citation: Computational Social Networks 2021 8:2
In literature, the machine learning-based studies of sentiment analysis are usually supervised learning which must have pre-labeled datasets to be large enough in certain domains. Obviously, this task is tedio...
Citation: Computational Social Networks 2021 8:1
Centrality measures have been proved to be a salient computational science tool for analyzing networks in the last two to three decades aiding many problems in the domain of computer science, economics, physic...
Citation: Computational Social Networks 2020 7:6
Strategic network formation is a branch of network science that takes an economic perspective to the creation of social networks, considering that actors in a network form links in order to maximise some utili...
Citation: Computational Social Networks 2020 7:5
The well-known minimum dominating set problem (MDSP) aims to construct the minimum-size subset of vertices in a graph such that every other vertex has at least one neighbor in the subset. In this article, we s...
Citation: Computational Social Networks 2020 7:4
The social networks are widely used by millions of people worldwide. The trust concept is one of the most important issues in Social Network Analysis (SNA) which highly affects the quantity and quality of the ...
Citation: Computational Social Networks 2020 7:3
The issue of polarization in online social media has been gaining attention in recent years amid the changing political landscapes of many parts of the world. Several studies empirically observed the existence...
Citation: Computational Social Networks 2020 7:2
Advising and mentoring Ph.D. students is an increasingly important aspect of the academic profession. We define and interpret a family of metrics (collectively referred to as “a-indices”) that can potentially be ...
Citation: Computational Social Networks 2020 7:1
We consider that a network of chaotic identical dynamical systems is connected to a new node. Depending on some properties of the network and on the way that connection is made, the new node may control the ne...
Citation: Computational Social Networks 2019 6:14
The formation of robust communication networks between independently acting agents is of practical interest in multiple domains, for example, in sensor placement and Unmanned Aerial Vehicle communication. Thes...
Citation: Computational Social Networks 2019 6:13
Many systems are today modelled as complex networks, since this representation has been proven being an effective approach for understanding and controlling many real-world phenomena. A significant area of int...
Citation: Computational Social Networks 2019 6:12
Graphs naturally appear in numerous application domains, ranging from social analysis, bioinformatics to computer vision. The unique capability of graphs enables capturing the structural relations among data, ...
Citation: Computational Social Networks 2019 6:11
Drug abuse continues to accelerate towards becoming the most severe public health problem in the United States. The ability to detect drug-abuse risk behavior at a population scale, such as among the populatio...
Citation: Computational Social Networks 2019 6:10
In the standard situation of networked populations, link neighbours represent one of the main influences leading to social diffusion of behaviour. When distinct attributes coexist, not only the network structu...
Citation: Computational Social Networks 2019 6:9
This paper proposes novel algorithms for efficiently counting complex network motifs in dynamic networks that are changing over time. Network motifs are small characteristic configurations of a few nodes and e...
Citation: Computational Social Networks 2019 6:8
Prerequisite inadequacy causes more MOOC drop-out. As an effective method interfering with learning process, existing MOOC recommendation is mainly about subsequent learning objects that have not been learned ...
Citation: Computational Social Networks 2019 6:7
In this paper, we address privacy issues related to ranked retrieval model in web databases, each of which takes private attributes as part of input in the ranking function. Many web databases keep private att...
Citation: Computational Social Networks 2019 6:6
In this paper, we develop a more general framework of block-structured Markov processes in the queueing study of blockchain systems, which can provide analysis both for the stationary performance measures and ...
Citation: Computational Social Networks 2019 6:5
Network equivalence is a technique useful for many areas including power systems. In many power system analyses, generation shift factor (GSF)-based bus clustering methods have been widely used to reduce the c...
Citation: Computational Social Networks 2019 6:4
Unstructured data generated from sources such as the social media and traditional text documents are increasing and form a larger proportion of unanalysed data especially in the developing countries. In this s...
Citation: Computational Social Networks 2019 6:3
Many algorithms require doing a large number of betweenness centrality calculations quickly, and accommodating this need is an active open research area. There are many different ideas and approaches to speedi...
Citation: Computational Social Networks 2019 6:2
Economists and social scientists have studied the human migration extensively. However, the complex network of human mobility in the United States (US) is not studied in depth. In this paper, we analyze migrat...
Citation: Computational Social Networks 2019 6:1
A dynamic influence spreading model is presented for computing network centrality and betweenness measures. Network topology, and possible directed connections and unequal weights of nodes and links, are essen...
Citation: Computational Social Networks 2018 5:12
The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting dense subgraphs inside the network. Here we ...
Citation: Computational Social Networks 2018 5:11
Internet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion t...
Citation: Computational Social Networks 2018 5:10
Hashtags are widely used for communication in online media. As a condensed version of information, they characterize topics and discussions. For their analysis, we apply methods from network science and propos...
Citation: Computational Social Networks 2018 5:9
The process of rumor spreading among people can be represented as information diffusion in social network. The scale of rumor spread changes greatly depending on starting nodes. If we can select nodes that con...
Citation: Computational Social Networks 2018 5:8
To identify potential stars in social networks, the idea of combining member promotion with skyline operator attracts people’s attention. Some algorithms have been proposed to deal with this problem so far, su...
Citation: Computational Social Networks 2018 5:7
Topic lifecycle analysis on social networks aims to analyze and track how topics are born from user-generated content, and how they evolve. Twitter researchers have no agreed-upon definition of topics; topics ...
Citation: Computational Social Networks 2018 5:6
Understanding traffic is an important challenge in different scientific fields. While there are many approaches to constructing traffic models, most of them rely on origin–destination data and have difficultie...
Citation: Computational Social Networks 2018 5:5
In the framework of network sampling, random walk (RW) based estimation techniques provide many pragmatic solutions while uncovering the unknown network as little as possible. Despite several theoretical advan...
Citation: Computational Social Networks 2018 5:4
Complex networks are found in many domains and the control of these networks is a research topic that continues to draw increasing attention. This paper proposes a method of network control that attempts to ma...
Citation: Computational Social Networks 2018 5:3
In this paper, we study the process of opinion dynamics and consensus building in online collaboration systems, in which users interact with each other following their common interests and their social profile...
Citation: Computational Social Networks 2018 5:2
Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives.
Citation: Computational Social Networks 2018 5:1
In this paper, we model the problem of influencing the opinions of groups of individuals as a containment control problem, as in many practical scenarios, the control goal is not full consensus among all the i...
Citation: Computational Social Networks 2017 4:12
We examine the coevolution of three-layer node-aligned network of university students. The first layer is defined by nominations based on perceived prominence collected from repeated surveys during the first f...
Citation: Computational Social Networks 2017 4:11
This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected a...
Citation: Computational Social Networks 2017 4:10
Recommendations are increasingly used to support and enable discovery, browsing, and exploration of items. This is especially true for entertainment platforms such as Netflix or YouTube, where frequently, no c...
Citation: Computational Social Networks 2017 4:9
The emerging grassroots party Barcelona en Comú won the 2015 Barcelona City Council election. This candidacy was devised by activists involved in the Spanish 15M movement to transform citizen outrage into poli...
Citation: Computational Social Networks 2017 4:8
Community discovery is an important task for revealing structures in large networks. The massive size of contemporary social networks poses a tremendous challenge to the scalability of traditional graph cluste...
Citation: Computational Social Networks 2017 4:7
Social media are an important source of information about the political issues, reflecting, as well as influencing, public mood. We present an analysis of Twitter data, collected over 6 weeks before the Brexit...
Citation: Computational Social Networks 2017 4:6
Highly dynamic social networks, where connectivity continuously changes in time, are becoming more and more pervasive. Knowledge mobilization, which refers to the use of knowledge toward the achievement of goa...
Citation: Computational Social Networks 2017 4:5
Influential actors detection in social media such as Twitter or Facebook can play a major role in gathering opinions on particular topics, improving the marketing efficiency, predicting the trends, etc.
Citation: Computational Social Networks 2017 4:3
Network-Oriented Modelling based on adaptive temporal–causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction proc...
Citation: Computational Social Networks 2017 4:4
Social networking services (SNSs) are widely used as communicative tools for a variety of purposes. SNSs rely on the users’ individual activities associated with some cost and effort, and thus it is not known ...
Citation: Computational Social Networks 2017 4:2
The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often as...
Citation: Computational Social Networks 2017 4:1
Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.
Citation: Computational Social Networks 2016 3:11
Named entity recognition (NER) is a task of detecting named entities in documents and categorizing them to predefined classes, such as person, location, and organization. This paper focuses on tweets posted on...
Citation: Computational Social Networks 2016 3:10
Why group opinions tend to be converged through continued communication, discussion and interactions? Under the framework of the social influence network model, we rigorously prove that the group consensus is ...
Citation: Computational Social Networks 2016 3:9
Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware inf...
Citation: Computational Social Networks 2016 3:8
Speed
121 days to first decision for reviewed manuscripts only
115 days to first decision for all manuscripts
262 days from submission to acceptance
19 days from acceptance to publication
Usage
31,574 downloads
26 Altmetric mentions
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