Social Network Representation Research Articles

Deep learning for automatic facial detection and recognition in Japanese macaques: illuminating social networks.

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research has offered new methodological perspectives through the automatisation of complex tasks. Harnessing object detection and recognition technologies is increasingly used by researchers to achieve identification on video footage. This study represents a preliminary exploration into the development of a non-invasive tool for face detection and individual identification of Japanese macaques (Macaca fuscata) through deep learning. The ultimate goal of this research is, using identification done on the dataset, to automatically generate a social network representation of the studied population. The current main results are promising: (i) the creation of a Japanese macaques' face detector (Faster-RCNN model), reaching an accuracy of 82.2% and (ii) the creation of an individual recogniser for the Kōjima Island macaque population (YOLOv8n model), reaching an accuracy of 83%. We also created a Kōjima population social network by traditional methods, based on co-occurrences on videos. Thus, we provide a benchmark against which the automatically generated network will be assessed for reliability. These preliminary results are a testament to the potential of this approach to provide the scientific community with a tool for tracking individuals and social network studies in Japanese macaques.

Axioms and Intuitions about Societal Inequality

We show that Corrado Gini’s “concentration ratio” is an informative measure of experienced inequality that (as he pointed out) varies from one (his “maximum concentration”) to zero (“minimum concentration”), a feature that does not hold (except in infinite populations) for the measure advocated in the contribution to this symposium by our colleague, Debraj Ray. Through a social network representation of inequality and a series of examples, we clarify the differing intuitions about the nature of inequality that alternative measures of inequality capture.

Imaginary network motifs: Structural patterns of false positives and negatives in social networks

We examine the structural patterns in the cognitive representation of social networks by systematically classifying false positives and negatives. Although existing literature on Cognitive Social Structures (CSS) has begun exploring false positives and negatives by comparing actual and perceived networks, it has not differentiated simultaneous occurrences of true and false positives and negatives on network motifs, such as reciprocity and triadic closure. Here, we propose a theoretical framework to categorize three classes of errors we call imaginary network motifs as combinations of accurately and erroneously perceived ties: (a) partially false, (b) completely false, and (c) mixed false. Using four published CSS data sets, we empirically test which imaginary network motifs are significantly more or less present in different types of perceived networks than the corresponding actual networks. Our results confirm that people not only fill in the blanks as suggested in the prior research but also conceive other imaginary structures. The findings advance our understanding of perception gaps between actual and perceived networks and have implications for designing more accurate network modeling and sampling.

Naval ship design-process analysis through dynamic social networks

ABSTRACT Modern naval ship design is increasing in complexity as more and more systems are incorporated into the design process, leading to an increase in the number (and interdependence) of tasks that designers need to complete and progress through the design stages. This work develops a dynamic bipartite social network representation, separating the nodes in activities and individuals, with the aim to analyse and draw conclusions regarding the design process. A dynamic time-dependent graph (TDG) is constructed with the use of a presence function for the edges. Network properties (density, clustering, active tasks/designers) are expressed as functions of time, and node-wise metrics (centralities) reveal the key role of individuals in the naval ship design process, which is heavily-crowded with respect to tasks. Furthermore, application of the model to naval ship design data reveals interesting insights regarding the impact of COVID-19 and the design company’s adopted hiring policy.

A heterogeneous graph neural recommendation model with hierarchical social trust

In traditional social recommendation algorithms, methods based on Graph Neural Networks (GNNs) representation of social networks are widely used, but the increasing sparsity of data and the tendency of users’ social influence to be complex make it difficult for the algorithms to improve the recommendation quality efficiently. To address the issues, a heterogeneous graph neural recommendation model with hierarchical social trust (GraphNON) is proposed. The algorithm is improved by fully exploiting user-friend relationships and modeling different propagation channels of social influence in multiple ways respectively. Firstly, different types of potential friends are mined based on user-user social graph and user-item rating graph respectively. Different dynamic influences of direct friends and indirectly friends among users are modeled through hierarchical influence propagation. Secondly, associations between items are mined from user-item rating graph, and influence transmission among items is modeled through hierarchical influence propagation. Finally, updated user characteristics and item characteristics are used to predict whether a user is interested in an item or not. It is shown on both datasets that the model has significant improvement in both hit rate (HR) and normalized discounted cumulative gain (NDCG) metrics compared to the optimal benchmark model. For a potential feature dimension of 64, the HR of the Yelp dataset improved by 18.3% and the NDCG by 29.3%, while the HR of the Flickr dataset improved by 31.4% and the NDCG by 37.1%.

An improved approach for ecological modeling of social phenomena in Blau space.

Advances in computation have opened new vistas for modeling of sociodemographic niches and related constructs, enabling us to rectify limitations that unavoidably plagued earlier generations of researchers. This is especially true for Blau space, a sociodemographic niche model used to explore competition between social entities over resources, such as memberships. While this approach has been successful in using probabilistic representations of social networks and resource niches, its modeling framework has remained essentially unchanged for over 40 years, and lacks the ability to make predictions about the future states of sociodemographic space. We present a novel Hybrid Blau space (HBS) model, which utilizes a cellular framework and probabilistic urn models to simulate competition over resources while suffering from fewer limitations. We apply this new model to the General Social Survey, running two sets of models from a series of variables (age, education, income, and sex) and utilize an adjustable range of sociodemographic information for local simulation of organizational competition. We also demonstrate the model's predictive ability, as well as introduce new methods of validation and fit.

Dynamics of social influence on consumption choices: A social network representation

In this work, through employing Friedkin Johnsen's model, we provide a valuable tool for understanding the complex dynamics of social influence and informational inducements in shaping consumption behaviour and highlight the need for governments, businesses, and individuals to address environmental concerns proactively. People mostly derive anticipation utility from consuming commodities through online shopping. Results suggest that in an information-loving society, people tend to follow the opinion of their groups, which can lead to inefficient choices. On the other hand, in a completely information-averse society, people tend to make inconsistent choices, leading to a lack of consensus. However, in a responsible society, individuals prioritise their own opinions and preferences while still taking into account the information and opinions of others. This results in a slow convergence of opinions, which can lead to responsible consumption and decision-making. People should be encouraged to form their own opinions based on their own experiences and preferences while still considering the information and opinions of others. It can lead to a more efficient and responsible society. Individuals with high self-confidence and self-control are more likely to resist peer pressure and make decisions that align with their values and goals. So, it is essential to consider the context and nature of the social influence when evaluating its impact on people's decision-making. Consumers are not the only players who can shape the world's future. Consumers, governments, corporations, and the media all have important roles to play, and their efforts must be coordinated and complementary to create a more sustainable future.

Social Experience Guidance for College Students’ Entrepreneurship in the Social Network

The low success rate of entrepreneurship is quite common for contemporary college students, and its main reason can be attributed to their lack of social experience and entrepreneurial guidance, thus, it’s of certain practical significance to study the social experience guidance for college students’ entrepreneurship and this paper aims to explore this problem based on social network. At first, this paper introduced the formation mechanism of the social experience guidance for college students’ entrepreneurship, proposed a novel Social Network Representation Learning (SNRL) method for college students’ entrepreneurship, which could attain more information of social experience guidance from networks with isomorphic substructures. Then, in the social network of college students’ entrepreneurship, this paper discussed the extraction method of structural subgraph of neighborhood space of college students and other entrepreneurial subject nodes, and proposed a method for building sub-networks similar to the scale and development state in the social network of college students’ entrepreneurship, and realized the information sharing of social and entrepreneurial experiences among sub-networks. At last, this paper constructed a Social and Entrepreneurial Experience Guidance (SEEG) model, and verified its effectiveness in experiments.

Adaptive Learning System of Ontology using Semantic Web to Mining Data from Distributed Heterogeneous Environment

Nowadays, the process of ontology learning for describing heterogeneous systems is an influential phenomenon to enhance the effectiveness of such systems using Social Network representation and Analysis (SNA). This paper presents a novel scenario for constructing adaptive architecture to develop community performance for heterogeneous communities as a case study. The crawling of the semantic webs is a new approach to create a huge data repository for classifying these communities. The architecture of the proposed system involves two cascading modules in achieving the ontology data, which is represented in Resource Description Framework (RDF) format. The proposed system improves the enhancement of these environments achieving both semantic web and SNA tools. Its contribution clearly appears on the community productions and skills developments. Python 3.9.0 platform was used for data pre-processing, feature extraction and clustering via Naïve Bayesian and support vector machine. Two case studies were conducted to test the accuracy rate of the proposed system. The accuracy rate for the case studies was (90.771%) and (90.1149 %) respectively, which is considered an affective precision when it is compared with the related scenario with the same data set.

MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter

To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE.

Association Rule Analysis-Based Identification of Influential Users in爐he燬ocial Media

The exchange of information is an innate and natural process that assist in content dispersal. Social networking sites emerge to enrich their users by providing the facility for sharing information and social interaction. The extensive adoption of social networking sites also resulted in user content generation. There are diverse research areas explored by the researchers to investigate the influence of social media on users and confirmed that social media sites have a significant impact on markets, politics and social life. Facebook is extensively used platform to share information, thoughts and opinions through posts and comments. The identification of influential users on the social web has grown as hot research field because of vast applications in diverse areas for instance political campaigns marketing, e-commerce, commercial and, etc. Prior research studies either uses linguistic content or graph-based representation of social network for the detection of influential users. In this article, we incorporate association rule mining algorithms to identify the top influential users through frequent patterns. The association rules have been computed using the standard evaluation measures such as support, confidence, lift, and conviction. To verify the results, we also involve conventional metrics for example accuracy, precision, recall and F1-measure according to the association rules perspective. The detailed experiments are carried out using the benchmark College-Msg dataset extracted by Facebook. The obtained results validate the quality and visibility of the proposed approach. The outcome of propose model verify that the association rule mining is able to generate rules to identify the temporal influential users on Facebook who are consistent on regular basis. The preparation of rule set help to create knowledge-based systems which are efficient and widely used in recent era for decision making to solve real-world problems.

Inferring Users' Social Roles with a Multi-Level Graph Neural Network Model.

Users of social networks have a variety of social statuses and roles. For example, the users of Weibo include celebrities, government officials, and social organizations. At the same time, these users may be senior managers, middle managers, or workers in companies. Previous studies on this topic have mainly focused on using the categorical, textual and topological data of a social network to predict users’ social statuses and roles. However, this cannot fully reflect the overall characteristics of users’ social statuses and roles in a social network. In this paper, we consider what social network structures reflect users’ social statuses and roles since social networks are designed to connect people. Taking an Enron email dataset as an example, we analyzed a preprocessing mechanism used for social network datasets that can extract users’ dynamic behavior features. We further designed a novel social network representation learning algorithm in order to infer users’ social statuses and roles in social networks through the use of an attention and gate mechanism on users’ neighbors. The extensive experimental results gained from four publicly available datasets indicate that our solution achieves an average accuracy improvement of 2% compared with GraphSAGE-Mean, which is the best applicable inductive representation learning method.

Identifying and Evaluating Anomalous Structural Change-based Nodes in Generalized Dynamic Social Networks

Recently, dynamic social network research has attracted a great amount of attention, especially in the area of anomaly analysis that analyzes the anomalous change in the evolution of dynamic social networks. However, most of the current research focused on anomaly analysis of the macro representation of dynamic social networks and failed to analyze the nodes that have anomalous structural changes at a micro level. To identify and evaluate anomalous structural change-based nodes in generalized dynamic social networks that only have limited structural information, this research considers undirected and unweighted graphs and develops a multiple-neighbor superposition similarity method ( ), which mainly consists of a multiple-neighbor range algorithm ( ) and a superposition similarity fluctuation algorithm ( ). introduces observation nodes, characterizes the structural similarities of nodes within multiple-neighbor ranges, and proposes a new multiple-neighbor similarity index on the basis of extensional similarity indices. Subsequently, maximally reflects the structural change of each node, using a new superposition similarity fluctuation index from the perspective of diverse multiple-neighbor similarities. As a result, based on and , not only identifies anomalous structural change-based nodes by detecting the anomalous structural changes of nodes but also evaluates their anomalous degrees by quantifying these changes. Results obtained by comparing with state-of-the-art methods via extensive experiments show that can accurately identify anomalous structural change-based nodes and evaluate their anomalous degrees well.

Penerapan Social Network Analysis Dalam Menganalisis Kerjasama Tokopedia Dengan Boyband Korea BTS

Tokopedia is an Indonesian technology company with a mission to achieve digitaleconomic equity. Since its founding in 2009, Tokopedia has transformed into aninfluential unicorn not only in Indonesia but also in Southeast Asia. On October 7,2019, Tokopedia announced a South Korean music group, BTS, to become the new brand ambassador for Tokopedia. BTS is a global mega star group from South Korea which is shaded by Big Hit Entertainment. Consisting of seven members including RM, Jin, SUGA, j-hope, Jimin, V, and Jung Kook, BTS was founded in 2013 and has had worldwide success. The extraordinary growth and achievements achieved by BTS have managed to break records in recent years so that BTS is designated as the persona of the Tokopedia brand. Through this collaboration, the public and BTS fans are expected to be closer to their inspirational figure. The various types of marketing currently carried out by Tokopedia in collaboration and collaboration with BTS have had a lot of impact on Tokopedia's sales. One of the marketing efforts that has been done by Tokopedia is using Twitter. Twitter is one of the social media used to attract consumers to buy products sold on Tokopedia. Using Social Network Analysis (SNA) provides a statistical tool for examining relational data not only on the characteristic attributes of individual actors, and focuses on explaining the patterns of relationships between actors, and analyzing the structure of these patterns. Social etwork representation is expressed in graph form because graph is the most fundamental type of social network representation. Social Network Analysis (SNA) argues that the relationship between nodes is important. The focus of Social Network Analysis (SNA) is on knowing the actors/nodes involved and how relationships occur. This study uses Social Network Analysis (SNA) to produce a structure of relationship data patterns between the collaboration between Tokopedia and Korean Boyband BTS which can help Tokopedia to review the collaboration that has been done. Tokopedia can take action in collaborating with BTS, to continuing or stopping or replacing with newbrand ambassadors.

JGraphT—A Java Library for Graph Data Structures and Algorithms

Mathematical software and graph-theoretical algorithmic packages to efficiently model, analyze, and query graphs are crucial in an era where large-scale spatial, societal, and economic network data are abundantly available. One such package is JGraphT, a programming library that contains very efficient and generic graph data structures along with a large collection of state-of-the-art algorithms. The library is written in Java with stability, interoperability, and performance in mind. A distinctive feature of this library is its ability to model vertices and edges as arbitrary objects, thereby permitting natural representations of many common networks, including transportation, social, and biological networks. Besides classic graph algorithms such as shortest-paths and spanning-tree algorithms, the library contains numerous advanced algorithms: graph and subgraph isomorphism, matching and flow problems, approximation algorithms for NP-hard problems such as independent set and the traveling salesman problem, and several more exotic algorithms such as Berge graph detection. Due to its versatility and generic design, JGraphT is currently used in large-scale commercial products, as well as noncommercial and academic research projects. In this work, we describe in detail the design and underlying structure of the library, and discuss its most important features and algorithms. A computational study is conducted to evaluate the performance of JGraphT versus several similar libraries. Experiments on a large number of graphs over a variety of popular algorithms show that JGraphT is highly competitive with other established libraries such as NetworkX or the BGL.

Social Networking among Women in Greek and Roman Comedy

This article examines the literary representation of women's social networks in comedy. I combine evidence from Aristophanes, Menander, Plautus, and Terence to answer two fundamental questions: 1) Who is in a woman's social network? 2) What activities do women engage in together? Finally, I discuss the literary representation of women's networks more broadly, focusing on four sample plays, which I selected for the size, strength, or composition of the women's social network. Because of the androcentric nature of the genre, women's relationships are imagined as analogous to or intertwined with those of men.

Double-Blinded Finder: a two-side secure children face recognition system

When taking photos of a suspicious missing child in the street and posting them to the social network is becoming a feasible way to find missing children, the exposure of photos may cause privacy issues. To address this problem, we propose Double-Blinded Finder, an efficient and double-blinded system for finding missing children via low-dimensional multi-attribute representation of child face and blind face matching. To obtain enough knowledge for representing child faces, we build the Labeled Child Face in the Wild dataset, which contains 60K Internet images with 6K unique identities. Based on this dataset, we further train a multi-task deep face model to describe a child face as a 128d fixed-point feature vector and extensive gender and age attributes. Using the keys generated from face descriptors, the face photos from the social network and face representation from the parent(s) of missing children are both encrypted. In addition, we devise inner-production encryption to run blind face matching in the public cloud. In this manner, Double-Blinded Finder can provide efficient face matching while protecting the privacies of both sides: (1) the suspicious missing children side for avoiding the invasion of the human rights, and (2) the true missing children side for preserving the secondary victimization. The experiments show that our system can achieve practical performance of child face matching with negligible leakage of privacy.

Graph-Based Prediction of Meeting Participation

Given a meeting participant’s turn-taking dynamics during one segment of a meeting, and their contribution to the group discussion up to that point, our aim is to automatically predict their activity level at a later point of the meeting. The predictive models use verbal and nonverbal features derived from social network representations of each small group interaction. The best automatic prediction models consistently outperform two baseline models at multiple time-lags. We analyze which interaction features are most predictive of later meeting activity levels, and investigate the efficacy of the verbal vs. nonverbal feature classes for this prediction task. At long time-lags, linguistic features become more crucial, but performance degrades compared with prediction at short time-lags.

Information Processing and Overload in Group Conversation: A Graph-Based Prediction Model

Based on analyzing verbal and nonverbal features of small group conversations in a task-based scenario, this work focuses on automatic detection of group member perceptions about how well they are making use of available information, and whether they are experiencing information overload. Both the verbal and nonverbal features are derived from graph-based social network representations of the group interaction. For the task of predicting the information use ratings, a predictive model using random forests with verbal and nonverbal features significantly outperforms baselines in which the mean or median values of the training data are predicted, as well as significantly outperforming a linear regression baseline. For the task of predicting information overload ratings, the multimodal random forests model again outperforms all other models, including significant improvement over linear regression and gradient boosting models. However, on that task the best model is not significantly better than the mean and median baselines. For both tasks, we analyze performance using the full multimodal feature set versus using only linguistic features or only turn-taking features. While utilizing the full feature set yields the best performance in terms of mean squared error (MSE), there are no statistically significant differences, and using only linguistic features gives comparable performance. We provide a detailed analysis of the individual features that are most useful for each task. Beyond the immediate prediction tasks, our more general goal is to represent conversational interaction in such a way that yields a small number of features capturing the group interaction in an easily interpretable manner. The proposed approach is relevant to many other group prediction tasks as well, and is distinct from both classical natural language processing (NLP) as well as more current deep learning/artificial neural network approaches.

Social Network Analysis of Protest Movements in Eastern Thailand

This article focuses on the social network analysis of 3 protest movements in eastern Thailand in order to investigate network structure, centrality, betweenness centrality, and network support. Social network data were collected in the form of survey questionnaires by a single-name generator. The data were analyzed by using UCINET and NetDraw Program for generating graphical representations of social networks. The research results reveal the structure of all protest movements are represented by a “Wheel/star structure”. Furthermore, the results of degree centrality and betweenness centrality demonstrate the high centrality with low segmentation of the networks. Lastly, the leaders endeavor to mobilize and strengthening movements by using personal ties in gaining network support.