Field Of Social Network Analysis Research Articles

Influence Maximization in Social Networks Using an Improved Multi-Objective Particle Swarm Optimization

Abstract The influence maximization (IM) problem has received great attention in the field of social network analysis, and its analysis results can provide reliable basis for decision makers when promoting products or political viewpoints. IM problem aims to select a set of seed users from social networks and maximize the number of users expected to be influenced. Most previous studies on the IM problem focused only on the single-objective problem of maximizing the influence spread of the seed set, ignoring the cost of the seed set, which causes decision makers to be unable to develop effective management strategies. In this work, the IM problem is formulated as a multi-objective IM problem that considers the cost of the seed set. An improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed to solve this problem. In the IMOPSO algorithm, the initialization strategy of Levy flight based on degree value is used to improve the quality of the initial solution, and the local search strategy based on greedy mechanism is designed to improve the Pareto Frontier distribution and promote algorithm convergence. Experimental results on six real social networks demonstrate that the proposed IMOPSO algorithm is effective, reducing runtime while providing competitive solutions.

Exploring collaborative innovation evolution of China’s energy conservation and environmental protection industry: a social network analysis based on patents

Purpose Energy conservation and environmental protection industry (ECEPI) is a strategic choice to promote energy conservation and emission reduction, develop green economy and circular economy. However, China’s ECEPI is still in the stage of rapid development and the overall scale is relatively small, what development periods have the ECEPI experienced? This study aims to contribute to a better understanding of collaborative innovation evolution based on social network analysis from the perspective of multi-dimensional proximity. Design/methodology/approach Methodologically, this study uses social network analysis method to explore the co-evolution of multidimensional collaboration networks. It divides China’s ECEPI into four periods based on national policies from 2001 to 2020. This contribution constructs collaborative innovation networks from geographical, technological and organizational proximity. Findings The results show that the collaborative innovation network was initially formed in the central region of China, gradually expanded to neighboring cities and the core positions of Beijing, Jiangsu and Guangdong have been continuously consolidated. C02F has been the core of the collaboration networks, and the research focus has gradually shifted from the treatment of wastewater, sewage or sludge to the separation field. Enterprises always occupy a dominant position in the collaboration networks. Originality/value This research investigates the dynamic evolution process of collaborative innovation network in China’s ECEPI from the perspective of multidimensional proximity, explores the community structure, important nodes and multidimensional proximity features in the network, expands the research perspective on evolution characteristics of innovative network and the research field of social network analysis. Theoretically, this study enriches collaborative innovation theory, social network theory and multi-dimensional proximity theory.

Dynamic Multi-view Group Preference Learning for group behavior prediction in social networks

Group behavior modeling is an important research topic in the field of social network analysis. Existing methods regarding this topic can only learn the static group preference, ignoring the multiple characteristics of the group behavior, so they cannot model the group behavior in a complete way. In this paper, we propose a Dynamic Multi-view Group Preference Learning (DMGPL) model for group behavior prediction in social networks. Firstly, an area-aware user dynamic preference extracting module is developed concerning user representation learning, which can integrate the dynamic user behavior preference and the corresponding group structural information into group members’ representations. Then, to simultaneously obtain stability, propensity, potentiality, and heterogeneity of the group behavior, Multi-view Group Preference Aggregation (MGA) is designed for group behavior modeling. In MGA, various aggregation strategies are used to capture different kinds of group behavior properties, which can achieve the effect of highlighting different properties in different scenarios. Moreover, considering the influence of the member’s size on group behavior, we define the group scaling degree and perform group representation scaling to make MGA more flexible. Finally, extensive experiments are performed on two real-world datasets, and the results show that the accuracy of group behavior prediction by DMGPL improved by about 10% compared to the baseline models.

Temporal-geographical attention-based transformer for point-of-interest recommendation

Point-of-Interest (POI) recommendation is one of the most important tasks in the field of social network analysis. Many efforts have been proposed to enhance the model performance for the POI recommendation task in recent years. Existing studies have revealed that the temporal factor and geographical factor are two crucial contextual factors which influence user decisions. However, they only learn representations of POIs and users from the single contextual factor and fuse the learned representations in the final stage, which ignores the interactions of different contextual factors, leading to learning suboptimal representations of POIs and users. To overcome this gap, we propose a novel Temporal-Geographical Attention-based Transformer (TGAT) for the POI recommendation task. Specifically, TGAT develops a hybrid sequence sampling strategy that samples the sequence of POIs from the different contextual factor POI graphs generated by the users’ check-in records. In this way, the interactions of different contextual factors can be care-fully pre-served. Then TGAT conducts a Transformer-based neural network backbone to learn representations of POIs from the sampling sequences. In addition, a weighted aggregation strategy is proposed to fuse the representations learned from different context factors. The extensive experimental results on real-world datasets have demonstrated the effectiveness of TGAT.

Locating community smells in software development processes using higher-order network centralities

Community smells are negative patterns in software development teams’ interactions that impede their ability to successfully create software. Examples are team members working in isolation, lack of communication and collaboration across departments or sub-teams, or areas of the codebase where only a few team members can work on. Current approaches aim to detect community smells by analysing static network representations of software teams’ interaction structures. In doing so, they are insufficient to locate community smells within development processes. Extending beyond the capabilities of traditional social network analysis, we show that higher-order network models provide a robust means of revealing such hidden patterns and complex relationships. To this end, we develop a set of centrality measures based on the MOGen higher-order network model and show their effectiveness in predicting influential nodes using five empirical datasets. We then employ these measures for a comprehensive analysis of a product team at the German IT security company genua GmbH, showcasing our method’s success in identifying and locating community smells. Specifically, we uncover critical community smells in two areas of the team’s development process. Semi-structured interviews with five team members validate our findings: while the team was aware of one community smell and employed measures to address it, it was not aware of the second. This highlights the potential of our approach as a robust tool for identifying and addressing community smells in software development teams. More generally, our work contributes to the social network analysis field with a powerful set of higher-order network centralities that effectively capture community dynamics and indirect relationships.

Dissilient interpersonal influences in social network analysis

Influence models are currently studied by researchers working in the field of social network analysis. The basic assumption in the existing literature is that the degree of interpersonal influence among two experts can be determined in a single meeting. The second presumption is that the degree of interpersonal influence determined in this meeting is stagnant and will not change in the subsequent meetings. In this paper, we assert that degree of interpersonal influence among two or more experts can change over time. One may not find the other person equally assertive in the second or third meeting. The idea is that it may take several meetings to finally be able to declare the degree to which an expert is influenced by others. We define a composition function for the matrix of interpersonal influence and use it to define the evolution process that this matrix goes through after every meeting. We also state the conditions under which the matrix of interpersonal influence converges in the long run.

A QLearning Approach for an efficient Estimation Of Initial Number of Communities in social networks

In modern society, social networks have become the main means of communication, enabling links to be created and facilitating the exchange of information between individuals. Consequently, the study of relationships and their impact on individuals, through social network analysis (SNA), has attracted a great deal of attention from researchers. In particular, communities, which play an essential role in the flow of information within a network, have been the subject of extensive study in the field of social network analysis. The difficulty with any community detection approach lies in the initialization stage, as the number of communities is unknown. This paper presents QESN, a new initialization approach for community detection based on QLearning, a reinforcement learning algorithm, which estimates the initial number of communities, thus providing an initial population of good quality, which is therefore improved using the Brain Storm Optimization metaheuristic. Through empirical studies conducted on seven social networks, we demonstrate the effectiveness of our approach, which outperforms state-of-the-art algorithms.

Men Think they Know More about Networks

Abstract The fields of social network analysis and network science have recently undertaken efforts to address issues of gender diversity and representation. However, despite these important efforts, subtle threats to representation are likely to persist. I use a 2023 global survey of 260 network researchers to show that men rate themselves as having more expertise with networks than women. This is troubling because, to the extent that women believe they lack the expertise to be considered for awards or collaboration opportunities, it can impede other efforts to diversify the field. I conclude by discussing some possible explanations for this finding and some possible strategies for addressing its consequences.

Network Alignment across Social Networks Using Multiple Embedding Techniques

Network alignment, which is also known as user identity linkage, is a kind of network analysis task that predicts overlapping users between two different social networks. This research direction has attracted much attention from the research community, and it is considered to be one of the most important research directions in the field of social network analysis. There are many different models for finding users that overlap between two networks, but most of these models use separate and different techniques to solve prediction problems, with very little work that has combined them. In this paper, we propose a method that combines different embedding techniques to solve the network alignment problem. Each association network alignment technique has its advantages and disadvantages, so combining them together will take full advantage and can overcome those disadvantages. Our model combines three-level embedding techniques of text-based user attributes, a graph attention network, a graph-drawing embedding technique, and fuzzy c-mean clustering to embed each piece of network information into a low-dimensional representation. We then project them into a common space by using canonical correlation analysis and compute the similarity matrix between them to make predictions. We tested our network alignment model on two real-life datasets, and the experimental results showed that our method can considerably improve the accuracy by about 10–15% compared to the baseline models. In addition, when experimenting with different ratios of training data, our proposed model could also handle the over-fitting problem effectively.

An Ontological Approach to Information Integration in Data Intensive Domains

The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. Firstly, new sources (e. g. online social networks, published databases) constantly become available for gathering, analyzing, and interpreting data. Thus, new sources should be modelled and embedded in existing data structures maintaining logical consistency. Secondly, new techniques and underlying algorithms are also constantly being developed. Therefore, analysis results should be integrated with source data, and metamodels that describe the integration should be adaptable and extensible. Thirdly, the dynamism of semi-structured objects entails constant changes in knowledge models produced by domain experts and knowledge engineers. Considering that the same data could be used by different domain experts it is crucial not only to support traceability of changes in models but also to ensure independence of expert interpretation of these models. The analysis of existing approaches to information integration shows lack of solutions implementing traceability of changes. This paper introduces a novel approach to information integration based on ontological and production knowledge models to fill this gap. A conceptual description of the approach and an underlying set-theoretical model are given. The main difference of the given approach from the existing ones is the uniformity to the integration of different kinds of ontologies as well as different versions of ontologies using rule-based model of ontological mappings, which is demonstrated by the example of solving the special case of the problem of identifying key users (so-called bridges) in social networks.

Identifying emerging smells in software designs based on predicting package dependencies

Software systems naturally evolve, and this evolution often brings design problems that contribute to system degradation. Architectural smells are typical symptoms of such problems, and several of these smells are related to undesired dependencies among packages. The early detection of smells is essential for software engineers to plan ahead for maintenance or refactoring efforts. Although tools for identifying smells exist, they detect the smells once they already exist in the source code when their undesired dependencies are already created. In this work, we explore a forward-looking approach for identifying smells that can emerge in the next system version based on inferring package dependencies that are likely to appear in the system. Our approach takes the current design structure of the system as a network, along with information from previous versions, and applies link prediction techniques from the field of social network analysis. In particular, we consider a group of smells known as instability smells (cyclic dependency, hub-like dependency, and unstable dependency), which fit well with the link prediction model. The approach includes a feedback mechanism to progressively reduce false positives in predictions. An evaluation based on six open-source projects showed that, under certain considerations, the proposed approach can satisfactorily predict missing dependencies and smell configurations thereof. The feedback mechanism led to improvements of up to three times the initial precision values. Furthermore, we have developed a tool for practitioners to apply the approach in their projects.

A label propagation-based method for community detection in directed signed social networks

Community detection is one of the most essential issues in social networks analysis field. Among the available categories of algorithms, the label propagation algorithm-based (LPA-based) methods, due to their proper time complexity, are of high concern. As all the social networks explicitly or implicitly include signed relationships, the attempt here is to suggest an LPA-based approach for community detection in the directed signed social networks. The direction of edges is not addressed in available LPA-based community detection methods for signed social networks. In this respect, 1) a weighting method is suggested in order to utilize the direction information that converts the network into an undirected weighted signed social network, 2) this weight is combined with a second weight obtained from the sign information of the edges, and 3) the LPA is extended, where the combined weights are applied in label propagation. Moreover, the directed signed modularity and the directed signed flow-based capacity measures are proposed. The findings of the run experiments indicate that the proposed method as to the directed signed modularity, directed signed flow-based capacity, and frustration measures on real-world and synthetic data sets, outperforms its counterparts.

Collaboration between authors in the field of social network analysis

Collaboration between authors in the field of social network analysis

Design Patterns for a Knowledge-Driven Analytical Platform

The development and support of knowledge-based systems for experts in the field of social network analysis (SNA) is complicated because of the problems of viability maintenance that inevitably emerge in data intensive domains. Largely this is the case due to the properties of semi-structured objects and processes that are analyzed by data specialists using data mining techniques and others automated analytical tools. In order to be viable a modern knowledge-based analytical platform should be able to integrate heterogeneous information, present it to users in an understandable way and to support tools for functionality extensibility. In this paper we introduce an ontological approach to information integration and propose design patterns for developing analytical platform core functionality such as ontology repository management, domain-specific languages (DSLs) generation and source code round-trip synchronization with DSL-models.

Homophily and Social Capital in a Network of Religious Congregations

This article investigates the relationship between homophily, the tendency for relationships to be more common among similar actors, and social capital in a social network of religious congregations from eight counties encompassing and surrounding a major metropolitan area in the southeastern United States. This network is inter-congregational, consisting of congregations and the relationships between them. Two types of social capital are investigated: the first involves the extent to which congregations bridge across structural holes, or bridge together otherwise disconnected congregations within the network; secondly, network closure involves the extent to which congregations are embedded in tight-knit clusters. Analyses use two types of homophily (religious and racial) to predict both outcomes, and they test linear and curvilinear relationships between both forms of homophily and the outcomes. Results indicate that congregations with moderate levels of religious homophily are more likely to bridge between otherwise disconnected congregations; however, congregations with low or high religious homophily as well as congregations with high racial homophily are more likely to be embedded in tight-knit relational clusters. This article contributes additional social network research on congregations and evidence of curvilinear relationships between homophily and social capital to the fields of social network analysis and sociology of religion.

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Social network aims to extend a widespread framework to communicate users and find alike people with common features, easier and faster. As people usually experience in everyday life, social communication can be formed from common groups with almost identical properties. Detecting such groups or communities is a challenging task in various fields of social network analysis. Many researchers intend to develop algorithms that work effectively and efficiently on social networks. It is believed that the most influential user in a community that had been followed by similar users could be a central point of a community or cluster, and the similar user would be members of the community. Research studies tend to increase intracommunity similarity and decrease intercommunity similarity to improve the performance of the community detection methods by finding such influential users accurately. In this paper, a hybrid metaheuristic method is proposed. In the proposed method called trust-based community detection using artificial bee colony by feature fusion (TCDABCF), we use a fusion approach combined with artificial bee colony (ABC) to improve the accuracy of the community detection task. In this approach, not only the social features of users are considered but also the relationship of trust between users in a community is also calculated. So, the proposed method can lead to finding more precise clusters of similar users with influential users in the center of each cluster. The proposed method uses the artificial bee colony (ABC) to find the influential users and the relation of their followers accurately. We compare this algorithm with nine state-of-the-art methods on the Facebook dataset. Experimental results show that the proposed method has obtained values of 0.9662 and 0.9533 for NMI and accuracy, respectively, which has improved in comparison with state-of-the-art community detection methods.

IM-ELPR: Influence maximization in social networks using label propagation based community structure

The popularity of social networks has grown manifolds in recent years because of various activities like fast propagation of ideas, publicity, and news. Influence maximization (IM) is one of the most highly studied problems in the field of social network analysis due to its business values. Influence maximization aims to identify influential nodes that can spread the information to the maximum number of nodes in the network through diffusion cascade. Traditional methods for IM include centrality based and greedy based measures. However, each method has some limitations. Recently, some methods of IM are introduced, which consider the presence of community structure in networks. Community structure has a significant impact on information diffusion, because of dense connections between the nodes in the community. In this paper, we propose a novel influence maximization algorithm using node seeding, label propagation, and community detection. We first use extended h-index centrality to detect the seed nodes and then use the label propagation technique to detect communities. Further, we merge smaller and related communities to a larger community with the help of a relationship matrix. Finally, top-k influential nodes from these communities are identified. These ideas lead to our proposed algorithm: Influence Maximization using Extended h-index, and Label Propagation with Relationship matrix (IM-ELPR). We adopt Independent Cascade (IC) information diffusion model to spread the information originating from chosen influential nodes. The proposed algorithm intends to identify influential nodes from different communities globally and does not depend on the specific community’s antecedent structural information. Experimental results performed on several real-life data sets reveal that the proposed algorithm performs better than many other existing popular algorithms.

Dismantling Networks by Skeleton Extraction and Greedy Tree Breaking

Network dismantling is one of the important NP-hard problems in the field of social network analysis. It aims to break down networks into many small components of limited size by only removing a small group of nodes. One feasible way is to decycle (eliminating all the cycles) the network first and then break the acyclic graph. However, existing decycling-based algorithms mainly concentrate on the decycling step, ignoring the importance of the tree breaking process. Besides, none of the algorithms try to pre-process the network, which may bring improvement in both effectiveness and efficiency. In this paper, we fill these two gaps by proposing a novel network dismantling algorithm that combines skeleton extraction and greedy tree breaking (SEGTB). Network skeleton supports the whole network structure, whose removal would leave a much looser structure and serves as an effective pre-processing for the dismantling problem. The presented tree breaking method is provided with theoretical proofs on its lower bound. Experiments on ten real-world datasets show that our proposed SEGTB algorithm is both effective and efficient, outperforming the state-of-the-art.

Assessing the sociability of former pet and entertainment chimpanzees by using multiplex networks

Advances in the field of social network analysis facilitate the creation of multiplex networks where several interaction types can be analysed simultaneously. In order to test the potential benefits of this approach, we investigated the sociability of atypically raised chimpanzees by constructing and analysing 4-layered multiplex networks of two groups of former pet and entertainment chimpanzees (Pan troglodytes). These networks are based on four social interaction types (stationary vicinity, affiliative behaviour, allogrooming, passive close proximity) representing low- to high-level interaction types in terms of sociability. Using the tools provided by the MuxViz software, we could assess and compare the similarity and information gain of each these social interaction types. We found some social interaction types to be more similar than other ones. However, each social interaction type imparted different information. We also tested for a possible impact of the chimpanzees’ biographical background on the social interaction types and found affiliative behaviour as well as allogrooming to be affected by adverse early life experiences. We conclude that this multiplex approach provides a more realistic framework giving detailed insight into the sociability of these chimpanzees and can function as a tool to support captive care management decisions.

Outbreak minimization v.s. influence maximization: an optimization framework

BackgroundAn effective approach to containing epidemic outbreaks (e.g., COVID-19) is targeted immunization, which involves identifying “super spreaders” who play a key role in spreading disease over human contact networks. The ultimate goal of targeted immunization and other disease control strategies is to minimize the impact of outbreaks. It shares similarity with the famous influence maximization problem studied in the field of social network analysis, whose objective is to identify a group of influential individuals to maximize the influence spread over social networks. This study aims to establish the equivalence of the two problems and develop an effective methodology for targeted immunization through the use of influence maximization.MethodsWe present a concise formulation of the targeted immunization problem and show its equivalence to the influence maximization problem under the framework of the Linear Threshold diffusion model. Thus the influence maximization problem, as well as the targeted immunization problem, can be solved by an optimization approach. A Benders’ decomposition algorithm is developed to solve the optimization problem for effective solutions.ResultsA comprehensive computational study is conducted to evaluate the performance and scalability of the optimization approach on real-world large-scale networks. Computational results show that our proposed approaches achieve more effective solutions compared to existing methods.ConclusionsWe show the equivalence of the outbreak minimization and influence maximization problems and present a concise formulation for the influence maximization problem under the Linear Threshold diffusion model. A tradeoff between computational effectiveness and computational efficiency is illustrated. Our results suggest that the capability of determining the optimal group of individuals for immunization is particularly crucial for the containment of infectious disease outbreaks within a small network. Finally, our proposed methodology not only determines the optimal solutions for target immunization, but can also aid policymakers in determining the right level of immunization coverage.