Classical Social Network Research Articles

Quantifying External Information in Social Network Analysis: An Application to Comparative Mythology.

Social network analysis is a popular tool to understand the relationships between interacting agents by studying the structural properties of their connections. However, this kind of analysis can miss some of the domain-specific knowledge available in the original information domain and its propagation through the associated network. In this work, we develop an extension of classical social network analysis to incorporate external information from the original source of the network. With this extension we propose a new centrality measure, the semantic value, and a new affinity function, the semantic affinity, that establishes fuzzy-like relationships between the different actors in the network. We also propose a new heuristic algorithm based on the shortest capacity problem to compute this new function. As an illustrative case study, we use the novel proposals to analyze and compare the gods and heroes from three different classical mythologies: 1) Greek; 2) Celtic; and 3) Nordic. We study the relationships of each individual mythology and those of the common structure that is formed when we fuse the three of them. We also compare our results with those obtained using other existing centrality measures and embedding approaches. In addition, we test the proposed measures on a classical social network, the Reuters terror news network, as well as in a Twitter network related to the COVID-19 pandemic. We found that the novel method obtains more meaningful comparisons and results than previous existing approaches in every case.

Theorizing the concept of social tie using frames

In classical Social Network Analysis (SNA), what counted as a “social tie” was fixed by available data collection methods. The emergence of large-scale unobtrusive data collection techniques has sparked renewed interest in the very idea of what counts as a “social tie.” Importantly, there has been an acknowledgment that the core issues raised by these developments are primarily conceptual. As a result, there is renewed interest in developing a scientifically grounded characterization of what is arguably the most central concept in social network analysis. This paper contributes to this conceptual effort. I rely on a technique of conceptual representation borrowed from cognitive psychology and cognitive linguistics in which frames for concepts are represented as directed graphs linking attributes to values. I show how the frame representation helps clarify the sort of claims that network theories make (e.g., imposing restrictions on attributes and values), how it helps specify both intra and inter-conceptual relations, how it illuminates seldom noted inter-theoretical commonalities and contrasts, and how it helps avoid common conceptual pitfalls.

Exploring the Teaching Mechanism of School Mental Health Education Based on Social Networks

Abstract This paper analyzes the social network, explores the association structure in the social network, optimizes the classical social network mining algorithm by combining the penalty matrix, and constructs the mining algorithm for students’ mental health status based on the social network by using the penalty matrix for clustering inference. On this basis, the constructed algorithm is verified by combining quality index, performance index and modularity, and the mining of students’ mental health status and mental health influencing factors is carried out to explore the teaching mechanism of school mental health education from the side. The results show that the mental health social network has a total of 50 nodes and 169 edges, and the module degree is generally in (0.4,0.9), which is reasonably divided, and is in the subhealth condition, and the mental disorder situation accounts for more. The correlation of students’ mental health is mostly distributed between (2,5), and the correlation coefficients of environmental changes, academic expectations, interpersonal relationships, self-perception, psychological conflicts, life events, family environment, and economic situation with students’ mental health are all greater than 0.5 so that the mechanism of teaching mental health education in schools is a home-school-society linkage mechanism.

A novel similarity measure of link prediction in bipartite social networks based on neighborhood structure

AbstractLink prediction is one of the methods of social network analysis. Bipartite networks are a type of complex network that can be used to model many natural events. In this study, a novel similarity measure for link prediction in bipartite networks is presented. Due to the fact that classical social network link prediction methods are less efficient and effective for use in bipartite network, it is necessary to use bipartite network-specific methods to solve this problem. The purpose of this study is to provide a centralized and comprehensive method based on the neighborhood structure that performs better than the existing classical methods. The proposed method consists of a combination of criteria based on the neighborhood structure. Here, the classical criteria for link prediction by modifying the bipartite network are defined. These modified criteria constitute the main component of the proposed similarity measure. In addition to low simplicity and complexity, this method has high efficiency. The simulation results show that the proposed method with a superiority of 0.5% over MetaPath, 1.32% over FriendLink, and 1.8% over Katz in thef-measure criterion shows the best performance.

Network Clustering Algorithm Based on Fast Detection of Central Node

Based on scale-free and density-based complex networks and numerical clustering algorithm, a graph clustering algorithm based on fast detection of central nodes is proposed. Through the calculation of local density and comprehensive clustering of nodes in the network, the clustering center in the network can be found quickly and noncentral nodes can be divided into the clustering center according to the nearest neighbor principle, thus avoiding parameter limitations such as the number of clustering to be set in advance using conventional classic social network detection algorithm. The experimental comparison and analysis in the real network indicate that the graph clustering algorithm based on fast detection of the central node is highly effective and efficient.

Comparative Agent-Based Simulations on Levels of Multiplicity Using a Network Regression: A Mobile Dating Use-Case

We demonstrate the use of agent-based models to simulate the interactions of two mobile dating applications that possess divergent interaction features. We reproduce several expected outcomes when compared to extant literature. We also demonstrate the use of a standard social network analysis technique—the network regression, Multiple Regression Quadratic Assignment Procedure—in conducting a principled and interpretable comparison between the two models with strong results. This combined approach is novel and allows complex system modelers who utilize agent-based models to reduce their reliance on idealized network structures (small world, scale-free, erdos-renyi) when applying underlying network interactions to agent-based models that can often skew results and mislead from a full picture of system-level properties. This work serves as a proof-of-concept in the integration of classical social network analysis methods and contemporary agent-based modeling to compare software designs and to enhance the policy-generation process of online social networks.

Dynamic network analysis of contact diaries

Analyzing two-mode networks linking actors to events they attend may help to uncover the structure and evolution of social networks. This classic social network insight is particularly valuable in the analysis of data extracted from contact diaries where contact events produce — and at the same time are the product of relations among participants. Contact events may comprise any number of actors meeting at a specific point in time. In this paper we recall the correspondence between two-mode actor–event networks and hypergraphs, and propose relational hyperevent models (RHEM) as a general modeling framework for networks of time-stamped multi-actor events in which the diarist (“ego”) simultaneously meets several of her alters. RHEM can estimate event intensities associated with each possible subset of actors that may jointly participate in events, and test network effects that may be of theoretical or empirical interest. Examples of such effects include preferential attachment, prior shared activity (familiarity), closure, and covariate effects explaining the propensity of actors to co-attend events. Statistical tests of these effects can uncover processes that govern the formation and evolution of informal groups among the diarist’s alters. We illustrate the empirical value of RHEM using data comprising almost 2000 meeting events of former British Prime Minister Margaret Thatcher with her cabinet ministers, transcribed from contact diaries covering her first term in office (1979–1983).

Combining Network Visualization and Data Mining for Tax Risk Assessment

This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: (i) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; (ii) an initial set of risky taxpayers is identified by applying machine learning algorithms; (iii) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; (iv) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency.

Quasi-Laplacian centrality: A new vertex centrality measurement based on Quasi-Laplacian energy of networks

The measuring of vertex centrality, which determines the importance of vertices in a network, has been one of key issues in network analysis. Many classical methods have been already presented, such as degree, closeness, betweenness and PageRank centrality etc. In this paper, a new vertex centrality measurement called Quasi-Laplacian centrality is proposed. Our main idea is that the importance (centrality) of a vertex v is reflected by the variation of the Quasi-Laplacian energy responding to the deletion of the vertex v from the network. Furthermore, we prove that the Quasi-Laplacian energy of a network G is related not only to the number of edges in the original network G but also to the number of edges in its corresponding line graph L(G). Thus, the new presented Quasi-Laplacian centrality of a vertex v considers not only its position in the original graph G, but also its position in the line graph L(G). We further investigate the validness and robustness of this new centrality measure by applying this method to three classical social network data sets and 14 other data sets from various domains. And on all these networks, we obtain reliable and even better results by using the common-used Susceptible–Infected–Resistant (SIR) model, which provide the strong evidences of the new measure’s utility. Besides, this new Quasi-Laplacian centrality measurement has lower computing complexity than others except degree centrality, thus is expected to have promising applications to run on big data in the future.

An UML to OWL based approach for extracting Moodle’s Ontology for Social Network Analysis

The main analyses in Social Network Analysis field are focusing on measuring the frequency of social interactions that are modeled only with numbers; 0 or 1 on the graph adjacency matrix. Therefore, ontologies have drawn attention to empower and enhance the Social Network Analysis with semantics. By exploring theses semantics, besides we better view and analyze the interactions between students and teachers inside the Learning Management System, the network graph structure and analyses change. In this paper, we propose a methodology to build Moodle’s OWL (Web Ontology Language) ontology for Social Network Analysis ends. The proposed ontology gives explicit meaning to the relationships inside Moodle instead of modeling them as nodes and edges with implicit semantics like the case in classical Social Network Analysis. The semantics of relationships influences the graph structure and statistics by making some nodes more central and influential and other nodes less central and influential. The built ontology is basically made by exploring the UML (Unified Modeling Language) class diagram that we created from Moodle Mount Orange School and is our main metadata source.

Egocentric Network Analysis. Foundations, Methods, and Models

Pocas veces he tenido la sensación de entender tantas cosas leyendo un libro como con Egocentric Network Analysis: Foundations, Methods, and Models, de Brea L. Perry, Bernice A. Pescosolido y Stephen P. Borgatti. Es uno de esos libros donde cada lector encuentra justo lo que estaba buscando, una definición, un concepto, una medida, una reflexión. Como el ya clásico Social Network Analysis: Methods and Applications de Stanley Wasserman y Katherine Faust (1994) éste es un libro que también se propone delinear y compendiar un campo teórico mirando al futuro. Y creo que lo consigue. Es justo decir que los autores han reunido de forma equilibrada tanto la reflexión teórica basada en una revisión autorizada de la literatura y sus métodos, como una agenda de investigación dentro del campo de la ciencia de las redes. Eran las personas adecuadas para hacerlo.

Follow the “Mastodon”: Structure and Evolution of a Decentralized Online Social Network

In this paper we present a dataset containing both the network of the "follow" relationships and its growth in terms of new connections and users, all which we obtained by mining the decentralized online social network named Mastodon. The dataset is combined with usage statistics and meta-data (geographical location and allowed topics) about the servers comprising the platform's architecture. These server are called instances. The paper also analyzes the overall structure of the Mastodon social network, focusing on its diversity w.r.t. other commercial microblogging platforms such as Twitter. Finally, we investigate how the instance-like paradigm influences the connections among the users. The newest and fastest-growing microblogging platform, Mastodon is set to become a valid alternative to established platforms like Twitter. The interest in Mastodon is mainly motivated as follows: a) the platform adopts an advertisement and recommendation-free business model; b) the decentralized architecture makes it possible to shift the control over user contents and data from the platform to the users; c) it adopts a community-like paradigm from both user and architecture viewpoints. In fact, Mastodon is composed of interconnected communities, placed on different servers; in addition, each single instance, with specific topics and languages, is independently owned and moderated. The released dataset paves the way to a number of research activities, which range from classic social network analysis to the modeling of social network dynamics and platform adoption in the early stage of the service. This data would also enable community detection validation since each instance hinges on specific topics and, lastly, the study of the interplay between the physical architecture of the platform and the social network it supports.

Recommender systems based on multiple social networks correlation

As development of social networks, social recommendation method, an effective information filtering technology, based on sociology rule and network theory, has improved performance of recommendation system and cold start problem. For data deep fusion and diverse development of social platform, the social relationship between users becomes more and more complex. The complexity of multiple social networks challenges social recommendation. However, most existing social recommendation methods focus on single social network, and multi-layer recommendation methods ignore nonlinearity and coupling between different social relationships. To tackle these problems, we propose a probabilistic matrix factorization model for multiply social networks joint recommendation framework based on joint probability distribution. This model analyzes different types of classic social networks and distribution function of user preferences similarity. Then we present unified model of recommendation based on social networks, as well as extensible multiply social networks joint recommendation method. The experimental results demonstrate comparing with relevant social recommendation algorithms; our method performs better on some evaluation indexes such as accuracy and errors.

Mining Resource Community and Resource Role Network From Event Logs

This paper deals with the perspective of organization based on process mining, which concentrates on activities and performers (resources) in event logs. A classic social network only reflects how closely related any two resource individuals are. Some resources may play similar roles in the network, and they can establish their own resource community. The algorithm reported in this paper constructs a resource community net with optimal modularity. Moreover, we find that there are some resources playing multiple roles in different communities, taking on different responsibilities in a system. Another algorithm is developed to discover these multi-role resources, and a corresponding resource role network is built. Finally, two examples are presented to demonstrate the proposed approach.

Eb&D: A new clustering approach for signed social networks based on both edge-betweenness centrality and density of subgraphs

Clustering algorithms for unsigned social networks which have only positive edges have been studied intensively. However, when a network has like/dislike, love/hate, respect/disrespect, or trust/distrust relationships, unsigned social networks with only positive edges are inadequate. Thus we model such kind of networks as signed networks which can have both negative and positive edges. Detecting the cluster structures of signed networks is much harder than for unsigned networks, because it not only requires that positive edges within clusters are as many as possible, but also requires that negative edges between clusters are as many as possible. Currently, we have few clustering algorithms for signed networks, and most of them requires the number of final clusters as an input while it is actually hard to predict beforehand. In this paper, we will propose a novel clustering algorithm called Eb&D for signed networks, where both the betweenness of edges and the density of subgraphs are used to detect cluster structures. A hierarchically nested system will be constructed to illustrate the inclusion relationships of clusters. To show the validity and efficiency of Eb&D, we test it on several classical social networks and also hundreds of synthetic data sets, and all obtain better results compared with other methods. The biggest advantage of Eb&D compared with other methods is that the number of clusters do not need to be known prior.

Hypergraph partitioning for social networks based on information entropy modularity

A social network is a typical scale-free network with power-law degree distribution characteristics. It demonstrates several natural imbalanced clusters when it is abstracted as a graph, and expands quickly under its generative mechanism. Hypergraph is superior for modeling multi-user operations in social networks, and partitioning the hypergraph modeled social networks could ease the scaling problems. However, today's popular hypergraph partitioning tools are not sufficiently scalable; thus, unable to achieve high partitioning quality for naturally imbalanced datasets. Recently proposed hypergraph partitioner, hyperpart, replaces the balance constraint with an entropy constraint to achieve high-fidelity partitioning solutions, but it is not tailored for scale-free networks, like social networks. In order to achieve scalable and high quality partitioning results for hypergraph modeled social networks, we propose a partitioning method, EQHyperpart, which utilizes information-Entropy-based modularity Q value (EQ) to direct the hypergraph partitioning process. This EQ considers power-law degree distribution while describing the “natural” structure of scale-free networks. We then apply simulated annealing and introduce a new definition of hyperedge cut, micro cut, to avoid the local minima in convergence of partitioning, developing EQHyperpart into two specific partitioners, namely: EQHyperpart-SA and EQHyperpart-MC. Finally, we evaluate the utility of our proposed method using classical social network datasets, including Facebook dataset. Findings show that EQHyperpart partitioners are more scalable than competing approaches, achieving a tradeoff between modularity retaining and cut size minimizing under balance constraints, and an auto-tradeoff without balance constraints for hypergraph modeled social networks.

Private Badges for Geosocial Networks

Geosocial networks (GSNs) extend classic online social networks with the concept of location. Users can report their presence at venues through “check-ins” and, when certain check-in sequences are satisfied, users acquire special status in the form of “badges”. We first show that this innovative functionality is popular in Foursquare, a prominent GSN. Furthermore, we address the apparent tension between privacy and correctness, where users are unable to prove having satisfied badge conditions without revealing the corresponding time and location of their check-in sequences. To this end, we propose several privacy preserving protocols that enable users to prove having satisfied the conditions of several badge types. Specifically, we introduce (i) GeoBadge and T-Badge, solutions for acquiring location badges, (ii) FreqBadge, for mayorship badges, (iii) e-Badge, for proving various expertise levels and (iv) MPBadge, for accumulating multi-player badges. We show that a Google Nexus One smartphone is able to perform tens of badge proofs per minute while a provider can support hundreds of million of check-ins and badge verifications per day.

Logical characterizations of regular equivalence in weighted social networks

Social network analysis is a methodology used extensively in social science. Classical social networks can only represent the qualitative relationships between actors, but weighted social networks can describe the degrees of connection between actors. In a classical social network, regular equivalence is used to capture the similarity between actors based on their links to other actors. Specifically, two actors are deemed regularly equivalent if they are equally related to equivalent others. The definition of regular equivalence has been extended to weighted social networks in two ways. The first definition, called regular similarity, considers regular equivalence as an equivalence relation that commutes with the underlying graph edges; while the second definition, called generalized regular equivalence, is based on the notion of role assignment or coloring. A role assignment (resp. coloring) is a mapping from the set of actors to a set of roles (resp. colors). The mapping is regular if actors assigned to the same role have the same roles in their neighborhoods. Recently, it was shown that social positions based on regular equivalence can be syntactically expressed as well-formed formulas in a kind of modal logic. Thus, actors occupying the same social position based on regular equivalence will satisfy the same set of modal formulas. In this paper, we present analogous results for regular similarity and generalized regular equivalence based on many-valued modal logics.

SOMSN: An Effective Self Organizing Map for Clustering of Social Networks

In this paper, a new algorithm named self-organizing map for clustering social networks (SOMSN) is proposed for detecting such groups. SOMSN is based on self-organizing map neural network. In SOMSN, by adapting new weightupdating method, a social network is divided into different clusters according to the topological connection of each node. These clusters are the communities that mentioned above, in social networks. To show the effectiveness of the presented approach, SOMSN has been applied on several classic social networks with known number of communities and defined structure. The results of these experiments show that the clustering accuracy of SOMSN is superior compared to the traditional algorithms.

Quantum social networks

We introduce a physical approach to social networks (SNs) in which each actor is characterized by a yes–no test on a physical system. This allows us to consider SNs beyond those originated by interactions based on pre-existing properties, as in a classical SN (CSN). As an example of SNs beyond CSNs, we introduce quantum SNs (QSNs) in which actor i is characterized by a test of whether or not the system is in a quantum state |ψi〉. We show that QSNs outperform CSNs for a certain task and some graphs. We identify the simplest of these graphs and show that graphs in which QSNs outperform CSNs are increasingly frequent as the number of vertices increases. We also discuss more general SNs and identify the simplest graphs in which QSNs cannot be outperformed.