Multilayer Social Network Research Articles

TLFSL: link prediction in multilayer social networks using trustworthy Lévy-flight semi-local random walk

Abstract As the landscape of online social networks continues to evolve, the task of expanding connections and uncovering novel relationships presents a growing complexity. Link prediction emerges as a crucial strategy, harnessing the current network dynamics to forecast future interactions among users. While traditional single-layer network link prediction models boast a storied legacy, recent attention has shifted towards tackling analogous challenges within the realm of multilayer networks. This paradigm shift underscores the critical role of extracting topological and multimodal features to effectively evaluate link weights, thereby enriching link prediction within weighted networks. Furthermore, the establishment of trustworthy pathways between users emerges as a pivotal tactic for translating unweighted similarities into meaningful weighted metrics. Leveraging the foundational principles of local random walk techniques, this paper introduces the trustworthy Lévy-flight semi-local (TLFSL) random walk framework for link prediction in multilayer social networks. By seamlessly integrating intralayer and interlayer information, TLFSL harnesses a dependable Lévy-flight random walk mechanism to anticipate new links within target layers of multilayer networks. Traditional local random walk techniques often overlook global relationships, as they confine path exploration to immediate neighbours. However, the absence of a direct edge between nodes does not necessarily imply a lack of relationship; nodes with semantic affinity may be spatially distant within the network. To overcome this limitation, we introduce the concept of semi-local random walk, which enables walker hopping with a wider global perspective. Meanwhile, TLFSL includes a distributed local community detection strategy to improve the performance of TLFSL in dealing with large-scale networks. Rigorous experimentation across diverse real-world multilayer networks consistently demonstrates TLFSL’s superior performance compared to equivalent methods.

Suspicious behaviour detection in multilayer social networks using PF-KMA and SS-GAE techniques

Multilayer Social networks are an important part of human life to interact on different networks at the same time. Due to the openness of such networks, they become a platform for spammers to spread malicious behaviors. Hence, there is an urgent need for effective detection of malicious behaviors; thereby, enabling the networks to take mitigation actions to decrease the possibility to reward such activities. Detection of suspicious behaviors in previous works is challenging due to the problems of community detection, the large amount of feature corruption, and memory requirements. Thus, to deal with such problems, in this paper, an efficient clustering-based detection of malicious users in multilayer social networks is proposed. Initially, the input dataset is pre-processed and used for Exponential Distribution based Erdős–Rényi based graph construction. From the graph structure, two types of data, such as user representations and graph features are extracted for graph encoding using the Soft Sign activated Graph Auto Encoder model. Then, the decoding is done to predict the information diffusion level, thereby, ranking the users using the Laplace Regularization technique. Then, the ranked users are clustered into different groups using the Pareto Front based K-Means Clustering Algorithm technique. Finally, the experimental results were analyzed to demonstrate the efficacy of the proposed model to detect malicious users in multilayer social networks.

Linking perspectives: a field experiment on the role of multi-layer networks in refugee information sharing

The social networks that interconnect groups of people are often “multi-layered”—comprised of a variety of relationships and interaction types. Although researchers increasingly acknowledge the presence of multiple layers and even measure them separately, little is known about whether and how different layers function differently. We conducted a field experiment in twelve villages in rural Uganda that measured real multi-layer social networks and then tracked their use in response to new, discussion-provoking information about refugees. We find that people who received our information treatment discussed refugees with more people, selected discussion partners from neighbors in the multi-layer network, and used most of the layers to do so. Treatment kicked off conversations throughout the villages that also included control respondents; treated and control both selected discussion partners from their networks who shared their attitudes towards refugees and were particularly interested in the subject. Our results point to multi-layer networks of day-to-day interactions as a source of prospective discussion partners when new information arises, especially layers based on shared meals, homestead visits, and money borrowing. When a relationship is based on multiple of these layers, it is even more likely to facilitate discussion. Furthermore, the selection of discussion partners from these networks depends less on any one particular layer and more on characteristics of the tie relative to the topic at hand.

Reliable multiplex semi-local random walk based on influential nodes to improve link prediction in complex networks

In recent years, the exponential growth of online social networks as complex networks has presented challenges in expanding networks and forging new connections. Link prediction emerges as a crucial technique to anticipate future relationships among users, leveraging the current network state to address this challenge effectively. While link prediction models on monoplex networks have a well-established history, the exploration of similar tasks on multilayer networks has garnered considerable attention. Extracting topological and multimodal features for weighting links can improve link prediction in weighted complex networks. Meanwhile, establishing reliable and trustworthy paths between users is a useful way to create metrics that convert unweighted to weighted similarity. The local random walk is a widely used technique for predicting links in weighted monoplex networks. The aim of this paper is to develop a semi-local random walk over reliable paths to improve link prediction on a multilayer social network as a complex network, which is denoted as Reliable Multiplex semi-Local Random Walk (RMLRW). RMLRW leverages the semi-local random walk technique over reliable paths, integrating intra-layer and inter-layer information from multiplex features to conduct a trustworthy biased random walk for predicting new links within a target layer of multilayer networks. In order to make RMLRW scalable, we develop a semi-local random walk-based network embedding to represent the network in a lower-dimensional space while preserving its original characteristics. Extensive experimental studies on several real-world multilayer networks demonstrate the performance assurance of RMLRW compared to equivalent methods. Specifically, RMLRW improves the average f-measure of the link prediction by 3.2% and 2.5% compared to SEM-Path and MLRW, respectively.

A comprehensive view of community detection approaches in multilayer social networks

A comprehensive view of community detection approaches in multilayer social networks

The latent cognitive structures of social networks

Abstract When people are asked to recall their social networks, theoretical and empirical work tells us that they rely on shortcuts, or heuristics. Cognitive social structures (CSSs) are multilayer social networks where each layer corresponds to an individual’s perception of the network. With multiple perceptions of the same network, CSSs contain rich information about how these heuristics manifest, motivating the question, Can we identify people who share the same heuristics? In this work, we propose a method for identifying cognitive structure across multiple network perceptions, analogous to how community detection aims to identify social structure in a network. To simultaneously model the joint latent social and cognitive structure, we study CSSs as three-dimensional tensors, employing low-rank nonnegative Tucker decompositions (NNTuck) to approximate the CSS—a procedure closely related to estimating a multilayer stochastic block model (SBM) from such data. We propose the resulting latent cognitive space as an operationalization of the sociological theory of social cognition by identifying individuals who share relational schema. In addition to modeling cognitively independent, dependent, and redundant networks, we propose a specific model instance and related statistical test for testing when there is social-cognitive agreement in a network: when the social and cognitive structures are equivalent. We use our approach to analyze four different CSSs and give insights into the latent cognitive structures of those networks.

Link prediction in multilayer networks using weighted reliable local random walk algorithm

The expansion of online social networks has led to attracting a significant number of users. Meanwhile, the analysis of these networks has raised new perspectives and challenges due to the large amount of information. One of these challenges in the analysis of social networks is the problem of link prediction. The purpose of this problem is to find links that have not yet been observed, but may exist in the future. There are many solutions for link prediction on monoplex networks. However, many real social networks model communication in multiple layers. In recent years, link prediction in multilayer social networks has attracted the attention of many researchers. A solution for multilayer networks involves taking into account the information of all layers to make predictions for a target layer. Among the link prediction techniques based on monoplex networks, local random walk is very popular. This paper presents an extended version of this technique for multilayer social networks considering reliable routes and weighted links. Our proposed algorithm as Weighted Reliable Local Random Walk (WRLRW) simultaneously uses interlayer and intralayer information to predict links in a target layer of a multilayer network. WRLRW adjusts link weights based on multimodal and topological features and forms reliable routes between users by multiplying link weights. We demonstrated the effectiveness of WRLRW to address the link prediction problem on real-world multilayer networks through numerical simulations. WRLRW has better performance compared to link prediction techniques based on monoplex networks as well as state-of-the-art methods based on multilayer networks. According to the results of the experiments, WRLRW obtains 3.2% and 2.5% better average f-measure compared to SEM-Path (Structural, Ego-Paths and Meta-Paths features) and MLRW (Multiplex Local Random Walk), respectively.

Link prediction using extended neighborhood based local random walk in multilayer social networks

One of these challenges in the analysis of social networks is the problem of link prediction. The purpose of this problem is to find links that have not yet been observed, but may exist in the future. There are many solutions for link prediction on monoplex networks. However, many real social networks model communication in multiple layers, which are known as multilayer social networks. A solution for multilayer networks involves taking into account the information of all layers to make predictions for a target layer. Among the existing solutions, local random walk has been confirmed as an efficient technique for link prediction in monoplex networks, but this technique is inefficient for link prediction in multilayer networks due to computational complexity. In order to address this issue, in this paper we propose Extended Neighborhood based Local Random Walk (ENLRW) for link prediction in multilayer networks. ENLRW is an extended version of the classical local random walk technique in which the nearest neighbors are considered based on the extended neighborhood concept. ENLRW calculates the similarity between vertices by integrating several different metrics through reliable paths that include intra-layer and inter-layer information. Besides, ENLRW considers vertex influence as a similarity metric to provide an effective reliable biased random walk. The results of the simulations show that the use of different inter-layer and intra-layer information as well as the local random walk configuration with extended neighborhood provides a trade-off between precision and complexity. Specifically, ENLRW improves the average precision by 3.1% compared to the best available state-of-the-art method.

Effect of three-stage cascade of opinion dynamics models in coupled networks

Information technology has contributed to the spread of public opinion across multiple coupled networks, and the scope of information spread has expanded. Owing to the impact of personal subjective cognition, information attenuation occurs during the process of opinion transmission in social networks, particularly in multi-layer social networks. In this paper, we construct a three-stage cascade opinion dynamics model, based on the Hegselmann–Krause (HK) model, to analyze the effect of information dissemination attenuation on group decision behavior in coupled social networks. This model considers three-stage cascade information transmission and two competitive opinion leader subgroups in two different social network environments, and can describe a public opinion dissemination environment more closely. The results show that the combination of the network structure and the level of trust of the public can effectively guide the spread of public opinion, which provides valuable insights into the role of social media in shaping public opinion and its impact on various fields. These findings can be useful for advertisers and policymakers to control public opinion and reach a consensus in group decision-making.

Towards a semi-local random walk technique through multilayer social networks to improve link prediction

Abstract The rapid expansion of social networks has generated a growing need for scalable algorithms capable of effectively predicting links. Link prediction is a crucial area of study within complex networks research. Link prediction aims to predict future connections between nodes from the current snapshot of the network and plays a vital role in estimating the growth of social networks. This article introduces an improved approach to link prediction in social networks by exploiting an extended version of local random walk as semi-local random walk (SLRW) for multilayer social networks. Here, taking into account the connectivity and structural similarity of the involved nodes, we propose the SLRW method to acquire nodes sequence with the highest similarity. Also, SLRW metric includes a distributed technique to identify the nearest neighbours by considering the extended neighbourhood concept. To ensure optimal performance, we conduct extensive studies on various hyperparameters of the proposed metric. The experimental results conducted on different datasets demonstrate that the proposed metric achieves improvements in the field of link prediction compared to the state-of-the-art baselines.

Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19

This study evaluates the scale-free network assumption commonly used in COVID-19 epidemiology, using empirical social network data from SARS-CoV-2 Delta variant molecular local clusters in Houston, Texas. We constructed genome-informed social networks from contact and co-residence data, tested them for scale-free power-law distributions that imply highly connected hubs, and compared them to alternative models (exponential, log-normal, power-law with exponential cutoff, and Weibull) that suggest more evenly distributed network connections. Although the power-law model failed the goodness of fit test, after incorporating social network ties, the power-law model was at least as good as, if not better than, the alternatives, implying the presence of both hub and non-hub mechanisms in local SARS-CoV-2 transmission. These findings enhance our understanding of the complex social interactions that drive SARS-CoV-2 transmission, thereby informing more effective public health interventions.

Research on the Interaction between Information and Behavior Based on Small Group Effect on Multilayer Social Networks

In the era of big data, massive amounts of information play an important role in individual behavior and decision-making. In order to investigate the interaction mechanism between information and individual behavior, we consider the influence of the "small group" network structure in social networks, and construct an information-behavior coupled dynamics propagation model (UAL-NBN) based on small group effect. Then we carry out theoretical analysis and derive the dynamic evolution equations for the model used the Micro Markov Chain Approach (MMCA). And we verify the correctness of the theoretical analysis by performing Monte Carlo simulations (MC). The results show that the small group effect does promote the spread of information and behavior in the population, which is reflected in reducing the epidemic threshold and increasing the outbreak size. In addition, we also conclude that the more small group structures exist in social networks, the more significant the promotion effect of the small group effect is. Finally, we describe the specific application of the model in scenarios such as epidemic control, rumor governance, social behavior advocacy, and consumer marketing, and provide theoretical reference and suggestions for the government and other relevant departments to formulate policies which promote the spread of behavior in society through information dissemination.

Decoupling in governance: the land governance network in a region of the Colombian Andes

Land governance requires coherence not only in terms of policies but also between its governance functions to achieve the desired goals. In this study, we focus on the functions that direct influence land use (boundary setting and resource appropriation; project formulation and financing; and monitoring, evaluation, and learning), which are expected to form a feedback loop necessary for adaptive co-management. We evaluated the degree of coherence of these three functions (as an approach to efficient management) via geo-located multilayer social network analysis and using an area of the Rio Grande and Chico basin in the Colombian Andes as the case study. According to the results, there is a conflict between production and conservation goals, necessitating collaboration among actors and institutions from various levels. The social network analysis revealed that the three functions are not articulated; instead, there exist two feedback loops (one per goal) in the boundary setting and resource appropriation function, leading to such conflict. The imposition of a governance system on the community by a few actors is recognized as the root of this conflict; hence, the need to move toward a governance with the community by promoting active participation in the various functions and interactions within them. We recommend future studies to assess the impact of governance networks on land-use actions to identify the reasons for land-use change and propose new strategies.

A Complex Insight for Quality of Service Based on Spreading Dynamics and Multilayer Networks in a 6G Scenario

Within the 6G vision, the future of mobile communication networks is expected to become more complex, heterogeneous, and characterized by denser deployments with a myriad of users in an ever-more dynamic environment. There is an increasing intent to provide services following the microservice architecture, thus gaining from higher scalability and significant reliability. Microservices introduce novel challenges and the level of granularity impacts performances, due to complex composition patterns. This openness in design demands service requirements be heterogeneous and dynamic. To this end, we propose a framework and a mathematical approach to investigate the complex quality of services. We exploit the temporal multilayer network representation and analysis jointly, with the spreading dynamics of user experience. We study the joint impact of structural heterogeneity and the evolutionary dynamics of the temporal multilayer quality network, composed of networked parameters, and a temporal multilayer social network, populated by a social layered structure of users. We conducted simulations to display our findings on how this modeling approach enables evaluation of otherwise-overlooked information on quality arising from a profound investigation of the structural-complexity and social-dynamics measurements.

House mouse subspecies do differ in their social structure.

It is widely acknowledged that population structure can have a substantial impact on evolutionary trajectories. In social animals, this structure is strongly influenced by relationships among the population members, so studies of differences in social structure between diverging populations or nascent species are of prime interest. Ideal models for such a study are two house mouse subspecies, Mus musculus musculus and M. m. domesticus, meeting in Europe along a secondary contact zone. Though the latter subspecies has usually been supposed to form tighter and more isolated social units than the former, the evidence is still inconclusive. Here, we carried out a series of radiofrequency identification experiments in semi-natural enclosures to gather large longitudinal data sets on individual mouse movements. The data were summarized in the form of uni- and multi-layer social networks. Within them, we could delimit and describe the social units ("modules"). While the number of estimated units was similar in both subspecies, domesticus revealed a more "modular" structure. This subspecies also showed more intramodular social interactions, higher spatial module separation, higher intramodular persistence of parent-offspring contacts, and lower multiple paternity, suggesting more effective control of dominant males over reproduction. We also demonstrate that long-lasting modules can be identified with basic reproductive units or demes. We thus provide the first robust evidence that the two subspecies differ in their social structure and dynamics of the structure formation.

A new multi-wave continuous action-set cellular learning automata for link prediction problem in weighted multi-layer social networks

A new multi-wave continuous action-set cellular learning automata for link prediction problem in weighted multi-layer social networks

A reproductive heir has a central position in multilayer social networks of paper wasps

Reproduction provides direct fitness benefits; therefore, it is important to determine why in some societies certain individuals have disproportionate access to reproductive opportunities. The social interactions that underlie reproductive hierarchies can occur in multiple situations, yet they are rarely studied in unison. The reproductive heir in the social wasp Ropalidia marginata is cryptic to human observers until the queen dies or disappears. To determine whether a reproductive heir can be identified through her behaviour, we examined four types of social situations: aggression, spatial overlap, trophallaxis and exchange of solid food. We asked whether accounting for all four social situations in a multilayer network provides more information about the structure of the society than examining each situation on its own or in an aggregate network that does not distinguish between social situations. We found that the reproductive heir had the most social interactions in the multilayer network but not in each of the social situations when considered separately or when all situations were lumped together. Our work demonstrates that multilayer networks can uncover new insights on social organization by explicitly considering the links between multiple situations of social interactions. • Social status is determined by synergies among interactions in multiple situations. • We use a novel multilayer network approach to identify a cryptic reproductive heir. • We test whether centrality in four social situations can predict an heir's identity. • Multilayer networks outperformed single or aggregate networks. • Multifaceted analysis of sociality provides previously unknown information.

From social networks to knowledge graphs: A plea for interdisciplinary approaches

Contextual information is a crucial factor in interdisciplinary research on social networks. At the same time, missing linked data and missing methods for large-scale knowledge networks are a serious limitation. Only few researches have addressed the questions between data science, graph theory and social network analysis. This paper seeks to discuss how this gap can be closed. For this, we use an interdisciplinary approach trying to improve the interdisciplinary exchange and method exchange. We investigate how new methods from computer science, in particular knowledge graphs, can be used within the field of SNA. The contributions of this paper are (1) a knowledge graph model representation of a multi-layer social network, (2) the showcase of examples from different domains and (3) a perspective for more interdisciplinary discussion. We present an innovative solution to discuss coming challenges in SNA with methods from the digital humanities, in particular computer science.

Multilayer social networks reveal the social complexity of a cooperatively breeding bird

SummaryThe social environment of individuals affects various evolutionary and ecological processes. Their social environment is affected by individual and environmental traits. We assessed the effects of these traits on nodes and dyads in six layers of networks of Arabian babblers, representing different interaction types. Additionally, we tested how traits affect social niches in the multilayer networks using the t-distributed stochastic neighbor embedding (tSNE) dimensionality reduction algorithm. The effect of group size and season was similar across network layers, but individual traits had different effects on different layers. Additionally, we documented assortativity with respect to individual traits in the dominance display and allopreening networks. The joint analysis of all six layers revealed that most traits did not affect individuals' social niches. However, older individuals occupied fewer social niches than younger ones. Our results suggest that multilayer social networks are an important tool for understanding the complex social systems of cooperative breeders and intragroup interactions.

Conceptualization of user activities in the social network in the conditions of distance education

In the article, social media is analyzed through the focus of understanding the latter as a virtual space of the media, which reflects such identifying indicators of users as: interest, desire, enthusiasm and direction of integration processes. The relevance of the study is determined by the insufficient development and inconsistency of the concepts and empirical results of research on the processes of differentiation of the community of social networks and their role in the conditions of distance education. The methods of philosophical, analysis and hermeneutics were used: interpretation, conceptualization, comparative analysis. As a theoretical and methodological base, we used the categorical apparatus of social philosophy, mathematics, theory of practice, pragmatism, social epistemology. We used approaches to extracting the activity of user groups in a multi-layer social network: 1) extracting groups in each layer separately, and then combining communities throughout the layers; 2) first transforming the social network into one layer, and then searching for different groups within.