Heterogeneous Social Networks Research Articles

Social Behavior Analysis in Exclusive Enterprise Social Networks by FastHAND

There is an emerging trend in the Chinese automobile industries that automakers are introducing exclusive enterprise social networks (EESNs) to expand sales and provide after-sale services. The traditional online social networks (OSNs) and enterprise social networks (ESNs), such as X (formerly known as Twitter) and Yammer, are ingeniously designed to facilitate unregulated communications among equal individuals. However, users in EESNs are naturally social stratified, consisting of both enterprise staffs and customers. In addition, the motivation to operate EESNs can be quite complicated, including providing customer services and facilitating communication among enterprise staffs. As a result, the social behaviors in EESNs can be quite different from those in OSNs and ESNs. In this work, we aim to analyze the social behaviors in EESNs. We consider the Chinese car manufacturer NIO as a typical example of EESNs and provide the following contributions. First, we formulate the social behavior analysis in EESNs as a link prediction problem in heterogeneous social networks. Second, to analyze this link prediction problem, we derive plentiful user features and build multiple meta-path graphs for EESNs. Third, we develop a novel Fast (H)eterogeneous graph (A)ttention (N)etwork algorithm for (D)irected graphs (FastHAND) to predict directed social links among users in EESNs. This algorithm introduces feature group attention at the node-level and uses an edge sampling algorithm over directed meta-path graphs to reduce the computation cost. By conducting various experiments on the NIO community data, we demonstrate the predictive power of our proposed FastHAND method. The experimental results also verify our intuitions about social affinity propagation in EESNs.

Adversarial Socialbots Modeling Based on Structural Information Principles

The importance of effective detection is underscored by the fact that socialbots imitate human behavior to propagate misinformation, leading to an ongoing competition between socialbots and detectors. Despite the rapid advancement of reactive detectors, the exploration of adversarial socialbot modeling remains incomplete, significantly hindering the development of proactive detectors. To address this issue, we propose a mathematical Structural Information principles-based Adversarial Socialbots Modeling framework, namely SIASM, to enable more accurate and effective modeling of adversarial behaviors. First, a heterogeneous graph is presented to integrate various users and rich activities in the original social network and measure its dynamic uncertainty as structural entropy. By minimizing the high-dimensional structural entropy, a hierarchical community structure of the social network is generated and referred to as the optimal encoding tree. Secondly, a novel method is designed to quantify influence by utilizing the assigned structural entropy, which helps reduce the computational cost of SIASM by filtering out uninfluential users. Besides, a new conditional structural entropy is defined between the socialbot and other users to guide the follower selection for network influence maximization. Extensive and comparative experiments on both homogeneous and heterogeneous social networks demonstrate that, compared with state-of-the-art baselines, the proposed SIASM framework yields substantial performance improvements in terms of network influence (up to 16.32%) and sustainable stealthiness (up to 16.29%) when evaluated against a robust detector with 90% accuracy.

C-privacy: a social relationship-driven image customization sharing method in cyber-physical networks

Cyber-Physical Networks(CPN) are comprehensive systems that integrate information and physical domains, and are widely used in various fields such as online social networking, smart grids, and the Internet of Vehicles(IoV). With the increasing popularity of digital photography and Internet technology, more and more users are sharing images on CPN. However, many images are shared without any privacy processing, exposing hidden privacy risks and making sensitive content easily accessible to Artificial Intelligence(AI) algorithms. Existing image sharing methods lack fine-grained image sharing policies and cannot protect user privacy. To address this issue, we propose a social relationship-driven privacy customization protection model for publishers and co-photographers. We construct a heterogeneous social information network centered on social relationships, introduce a user intimacy evaluation method with time decay, and evaluate privacy levels considering user interest similarity. To protect user privacy while maintaining image appreciation, we design a lightweight face-swapping algorithm based on Generative Adversarial Network(GAN) to swap faces that need to be protected. Our proposed method minimizes the loss of image utility while satisfying privacy requirements, as shown by extensive theoretical and simulation analyses.

MODELING AND ANALYZING DYNAMIC INFORMATION PROPAGATION ON SINA WEIBO IN A SEMI-DIRECTED NETWORK

In reality, the dissemination of information about COVID-19 vaccines typically involves a combination of opinion leaders and self-organizing networks, with each node being exposed to information in varying ways. However, conventional models often assume homogeneity in networks, treating all nodes as equal in terms of propagation probabilities within a fixed timeframe, thereby neglecting the inherent heterogeneity of social networks in information dissemination. To address this limitation, we propose a novel semi-directed network model, referred to as the susceptible-forwarding-immune model, which incorporates the complex structure of actual social networks and classifies nodes based on their mode of contact and the number of users they reach within a specific period. We calibrated and validated our model using real data on COVID-19 vaccine information from the Chinese Sina microblog, and our sensitivity analysis yielded insights into optimal strategies for disseminating such information.

Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social Networks

Link prediction is a critical prerequisite and foundation task for social network security that involves predicting the potential relationship between nodes within a network or graph. Although the existing methods show promising performance, they often ignore the unique attributes of each link type and the impact of diverse node differences on network topology when dealing with heterogeneous information networks (HINs), resulting in inaccurate predictions of unobserved links. To overcome this hurdle, we propose the Enhancing Predictive Expert Method (EPEM), a comprehensive framework that includes an individual feature projector, a predictive expert constructor, and a trustworthiness investor. The individual feature projector extracts the distinct characteristics associated with each link type, eliminating shared attributes that are common across all links. The predictive expert constructor then creates enhancing predictive experts, which improve predictive precision by incorporating the individual feature representations unique to each node category. Finally, the trustworthiness investor evaluates the reliability of each enhancing predictive expert and adjusts their contributions to the prediction outcomes accordingly. Our empirical evaluations on three diverse heterogeneous social network datasets demonstrate the effectiveness of EPEM in forecasting unobserved links, outperforming the state-of-the-art methods.

A Multi-Type Transferable Method for Missing Link Prediction in Heterogeneous Social Networks

Heterogeneous social networks, which are characterized by diverse interaction types, have resulted in new challenges for missing link prediction. Most deep learning models tend to capture type-specific features to maximize the prediction performances on specific link types. However, the types of missing links are uncertain in heterogeneous social networks; this restricts the prediction performances of existing deep learning models. To address this issue, we propose a multi-type transferable method ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> ) for missing link prediction in heterogeneous social networks, which exploits adversarial neural networks to remain robust against type differences. It comprises a generative predictor and a discriminative classifier. The generative predictor can extract link representations and predict whether the unobserved link is a missing link. To generalize well for different link types to improve the prediction performance, it attempts to deceive the discriminative classifier by learning transferable feature representations among link types. In order not to be deceived, the discriminative classifier attempts to accurately distinguish link types, which indirectly helps the generative predictor judge whether the learned feature representations are transferable among link types. Finally, the integrated <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> is constructed on this minimax two-player game between the generative predictor and discriminative classifier to predict missing links based on transferable feature representations among link types. Extensive experiments show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MTTM</i> can outperform state-of-the-art baselines for missing link prediction in heterogeneous social networks.

Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networks

Trust prediction facilitates the day-to-day functionality of diverse web-based applications, such as recommendation systems, market advertising and anomaly detection. However, existing works heavily rely on user–user trust interactions, which result in limited performance as the data sparsity. Previous studies have shown that the trust relationship between users is significantly affected by the category of items that the users interacted. In this paper, we propose a MetaTrust model, which generates redundant user-item interactions as the supplement of user–user trust to alleviate the data sparsity on trust prediction. Specifically, we propose category-aware metapaths, which generate abundant user–item–user interactions based on the common item category that users have interacted with. Further, Long Short Term Memory (LSTM) networks are utilized to mine features of multiple category-aware metapaths and their correlations. In order to filter the user–item–user interactions that are not related to the current task, the real trust relationship between users are embedd in the network with MLP. Finally, a multi-headed attention network is utilized to distinguish which metapath determines trust prediction between the current pair of users. Extensive experiments on three real-world dataset show that our proposed model can effectively achieve significant improvements over other competitive approaches and show the potential interpretability of trust building.

Forecasting the Missing Links in Heterogeneous Social Networks

Social network analysis has gained attention from several researchers in the past time because of its wide application in capturing social interactions. One of the aims of social network analysis is to recover missing links between the users which may exist in the future but have not yet appeared due to incomplete data. The prediction of hidden or missing links in criminal networks is also a significant problem. The collection of criminal data from these networks appears to be incomplete and inconsistent which is reflected in the structure in the form of missing nodes and links. Many machine learning algorithms are applied for this detection using supervised techniques. But, supervised machine learning algorithms require large datasets for training the link prediction model for achieving optimum results. In this research, we have used a Facebook dataset to solve the problem of link prediction in a network. The two machine learning classifiers applied are LogisticRegression and K-Nearest Neighbour where KNN has higher accuracy than LR. In this article, we have proposed an algorithm Graph Sample Aggregator with Low Reciprocity, (GraphSALR), for the generation of node embeddings in larger graphs which use node feature information.

The influence of social network on innovation and entrepreneurship of economic and management college students based on adaptive particle swarm algorithm

Abstract Exploring the influence of social networks on innovation and entrepreneurship of economics and management college students to help them better enhance their innovation consciousness to face the future social workplace. In this paper, the particle swarm algorithm is optimized according to the strategy and parameter adaptive mechanism, and the SPS-PSO algorithm is used to initialize and update the population. The social network and innovation and entrepreneurship ability of university students in economics and management are introduced, and feature selection based on strategy and parameter adaptive particle swarm algorithm is utilized. The index mining of social network and innovation and entrepreneurship ability of college students is conducted, and the data analysis of innovation and entrepreneurship ability of college students in economics and management is carried out with the example of the University of S. From the social network, the percentages of those who agree with the validity of social network centrality, heterogeneity, dynamics and linkage strength are 71.56, 71.69%, 72.55% and 71.16%, respectively. From the perspective of innovation and entrepreneurship, the overall percentage of college students in economics and management who agreed was 69.27%. This indicates that the higher the integrity of the social network structure of economics and management college students, the more it can enhance innovation and entrepreneurship abilities and promote the cultivation of observation and judgment ability and divergent thinking abilities of economics and management college students.

TDAN: Transferable Domain Adversarial Network for Link Prediction in Heterogeneous Social Networks

Link prediction has received increased attention in social network analysis. One of the unique challenges in heterogeneous social networks is link prediction in new link types without verified link information, such as recommending products to new overseas groups. Existing link prediction models tend to learn type-specific knowledge on specific link types and predict missing or future links on the same link types. However, because of the uncertainty of new link types in the evolving process of social networks, it is difficult to collect sufficient verified link information in new link types. Therefore, we propose the Transferable Domain Adversarial Network ( TDAN ) based on transfer learning to handle the challenge. TDAN exploits transferable type-shared knowledge in historical link types to help predict the unobserved links in new link types. TDAN mainly comprises a structural encoder, a domain discriminator, and an optimization decoder. The structural encoder learns the link representations in a heterogeneous social network. Subsequently, to learn transferable type-shared knowledge, the domain discriminator distinguishes link representations into different link types while minimizing the differences between type-specific knowledge in adversarial training. Inspired by the denoising auto-encoder, the optimization decoder reconstructs the learned type-shared knowledge to eliminate the noise generated during the adversarial training. Extensive experiments on Facebook and YouTube show that TDAN can outperform the state-of-the-art models.

Universal patterns in egocentric communication networks

Tie strengths in social networks are heterogeneous, with strong and weak ties playing different roles at the network and individual levels. Egocentric networks, networks of relationships around an individual, exhibit few strong ties and more weaker ties, as evidenced by electronic communication records. Mobile phone data has also revealed persistent individual differences within this pattern. However, the generality and driving mechanisms of social tie strength heterogeneity remain unclear. Here, we study tie strengths in egocentric networks across multiple datasets of interactions between millions of people during months to years. We find universality in tie strength distributions and their individual-level variation across communication modes, even in channels not reflecting offline social relationships. Via a simple model of egocentric network evolution, we show that the observed universality arises from the competition between cumulative advantage and random choice, two tie reinforcement mechanisms whose balance determines the diversity of tie strengths. Our results provide insight into the driving mechanisms of tie strength heterogeneity in social networks and have implications for the understanding of social network structure and individual behavior.

Community-Based Influence Maximization Using Network Embedding in Dynamic Heterogeneous Social Networks

Influence maximization (IM) is a very important issue in social network diffusion analysis. The topology of real social network is large-scale, dynamic, and heterogeneous. The heterogeneity, and continuous expansion and evolution of social network pose a challenge to find influential users. Existing IM algorithms usually assume that social networks are static or dynamic but homogeneous to simplify the complexity of the IM problem. We propose a community-based influence maximization algorithm using network embedding in dynamic heterogeneous social networks. We use DyHATR algorithm to obtain the propagation feature vectors of network nodes, and executek-means cluster algorithm to transform the original network into a coarse granularity network (CGN). On CGN, we propose a community-based three-hop independent cascade model and construct the objective function of IM problem. We design a greedy heuristics algorithm to solve the IM problem with\((1-\frac{1}{e})-\)approximation guarantee and use community structure to quickly identify seed users and estimate their influence value. Experimental results on real social networks demonstrated that compared with existing IM algorithms, our proposed algorithm had better comprehensive performance with respect to the influence value, more less execution time and memory consumption, and better scalability.

Influence of Agricultural Technology Extension and Social Networks on Chinese Farmers’ Adoption of Conservation Tillage Technology

Agricultural technology extension and social networks are the essential components of formal and informal institutions, respectively, and their influence on agricultural production has been the focus of academics. This article takes conservation tillage technology as an example, based on simple random unduplicated sampling, and uses survey data of 781 farmers in Heilongjiang, Henan, Shandong, and Shanxi provinces of China. This article empirically tests the interaction effects and heterogeneity of agricultural technology extension and social networks on farmers’ adoption of conservation tillage technology and analyzes their substitution effect or complementary effect. The results showed the following: (1) From a single dimension, both agricultural technology extension and social networks can significantly promote farmers’ adoption of conservation tillage technology, and the promotion effect of agricultural technology extension is greater. The average probability of farmers who accept agricultural technology extension and social networks adopting conservation tillage technology increases by 36.49% and 7.09%, respectively. (2) There is a complementary effect between agricultural technology extension and social networks in promoting farmers’ adoption of conservation tillage technology. The two functions complement and support each other, and this complementary effect is more evident in social networks’ reciprocity. (3) Further analysis reveals that the interaction effect between agricultural technology extension and social networks has significant group differences, technology type differences, and regional differences in farmers’ adoption of conservation tillage technology. Therefore, to facilitate the extension and application of conservation tillage technology, efforts need to be made in both agricultural technology extension and social networks, fully leveraging the complementary effects of the two. In addition, differentiated policies and measures should be adopted according to local conditions, and precise policies should be implemented for different groups and technologies.

Meta-learning adaptation network for few-shot link prediction in heterogeneous social networks

Link prediction, which aims to predict future or missing links among nodes, is a crucial research problem in social network analysis. A unique few-shot challenge is link prediction on newly emerged link types without sufficient verification information in heterogeneous social networks, such as commodity recommendation on new categories. Most of current approaches for link prediction rely heavily on sufficient verified link samples, and almost ignore the shared knowledge between different link types. Hence, they tend to suffer from data scarcity in heterogeneous social networks and fail to handle newly emerged link types where has no sufficient verified link samples. To overcome this challenge, we propose a model based on meta-learning, called the meta-learning adaptation network (MLAN), which acquires transferable knowledge from historical link types to improve the prediction performance on newly emerged link types. MLAN consists of three main components: a subtask slicer, a meta migrator, and an adaptive predictor. The subtask slicer is responsible for generating community subtasks for the link prediction on historical link types. Subsequently, the meta migrator simultaneously completes multiple community subtasks from different link types to acquire transferable subtask-shared knowledge. Finally, the adaptive predictor employs the parameters of the meta migrator to fuse the subtask-shared knowledge from different community subtasks and learn the task-specific knowledge of newly emerged link types. Experimental results conducted on real-world social media datasets prove that our proposed MLAN outperforms state-of-the-art models in few-shot link prediction in heterogeneous social networks.

A fusion recommendation model based on mutual information and attention learning in heterogeneous social networks

Heterogeneous social networks generate vast amounts of data due to their large user base. In response, recommendation systems have been developed as an effective tool to mitigate the problem of information overload. Nonetheless, standalone recommendation methods are often limited by their own inherent deficiencies. Consequently, deep learning has emerged as a powerful approach to address these issues. By integrating diverse recommendation methods, the shortcomings of individual methods can be partially compensated for and improved recommendation outcomes can be achieved. This paper presents a novel user interest fusion recommendation model called MIAL, which leverages mutual information and attention-based learning mechanisms. Specifically, a user’s posts are first transformed into word sequences via a Convolution Neural Network (CNN) based interest recommendation model for pre-processing. To enhance the multi-label prediction model, this study adopts a label selection method based on the label maximum mutual information spanning tree (LMMIST) to determine the final recommended label set. To identify reliable user similarity, the concept of pseudo-similar friends is introduced to construct heterogeneous networks. During the network embedding phase, a new random walk algorithm termed MRWalk and a pseudo-similar propagation mechanism are proposed. Experimental results using the Twitter dataset and public Amazon product dataset demonstrate that the proposed MIAL algorithm outperforms various state-of-the-art algorithms. The comparative analysis shows that MIAL has significantly improved performance.

MSR72 Sexually Transmitted Diseases Outbreak Prediction and Transmission Dynamics Understanding Using Agent-Based Modeling: Take the Global Spread of Male-Dominated and Msm-Dominated Mpox Outbreak As an Example

Heterogeneous social networks influence the spread and demographics of sexually transmitted infections (STIs). Mpox is a contemporary global sexually transmitted disease. The disease mostly affects males, and its main mode of transmission is through men who have sex with men (MSM). This research aims to explore the predicted outbreak of sexually transmitted diseases and transmission dynamics using an agent-based model.

Data-driven evolutionary game models for the spread of fairness and cooperation in heterogeneous networks.

Unique large-scale cooperation and fairness norms are essential to human society, but the emergence of prosocial behaviors is elusive. The fact that heterogeneous social networks prevail raised a hypothesis that heterogeneous networks facilitate fairness and cooperation. However, the hypothesis has not been validated experimentally, and little is known about the evolutionary psychological basis of cooperation and fairness in human networks. Fortunately, research about oxytocin, a neuropeptide, may provide novel ideas for confirming the hypothesis. Recent oxytocin-modulated network game experiments observed that intranasal administration of oxytocin to a few central individuals significantly increases global fairness and cooperation. Here, based on the experimental phenomena and data, we show a joint effect of social preference and network heterogeneity on promoting prosocial behaviors by building evolutionary game models. In the network ultimatum game and the prisoner's dilemma game with punishment, inequality aversion can lead to the spread of costly punishment for selfish and unfair behaviors. This effect is initiated by oxytocin, then amplified via influential nodes, and finally promotes global cooperation and fairness. In contrast, in the network trust game, oxytocin increases trust and altruism, but these effects are confined locally. These results uncover general oxytocin-initiated mechanisms underpinning fairness and cooperation in human networks.

An Online Topic Diffusion Prediction Approach on Heterogeneous Social Networks

Online topic indicates the diffusing process of various attitudes and emotions towards some events expressing and propagating over the Internet. It is an essential for monitoring all online information and ensuring internet information safety to search for the predicting approach of the trend of online topic. Previous methods focus on special social network and lack of versatility because of intensely relying on manual experience, that we are devoted to conquering these shortages. In the manuscript, we divide online topic into unimodal topic and multimodal topic which is the combination of a series of unimodal topics. We present a generally nonlinear dynamic model to describe unimodal topic diffusion mechanism on heterogeneous social network. The model can capture the long-term development tendency of unimodal topic, So, we present a long-term tendency prediction method of unimodal online topic with help of the dynamic model. But the model is not for multimodal online topic in that their long-term development tendency is made up of a series of unimodal topics’ long-term development tendency, which leads to the unpredictability of their long-term future directions, we propose a short-term population prediction method of unimodal and multimodal online topic diffusion on heterogeneous social network based on the essential relationship between unimodal and multimodal topic. Experimental results are our two methods have good performance on predicting the long-term development tendency of unimodal topic and all topic’s short-term population.

Embedding-based Method for the Supervised Link Prediction in Social Networks

In recent years, social network analysis has received a lot of interest. Link prediction is an important area of research in this field that uses information from current networks to predict the likely links that will emerge in the future. It has attracted considerable attention from interdisciplinary research communities due to its ubiquitous applications in biological networks, computer science, transportation networks, bioinformatics, telecommunication networks, and so on. Currently, supervised machine learning is one of the critical techniques in the link prediction task. Several algorithms have been developed by many authors to predict the future link in the network, but there is still scope to improve the previous approaches. In the supervised link prediction process, feature selection is a crucial step. Most existing algorithms use one type of similarity-based feature to represent data, which is not well described due to the large scale and heterogeneity of social networks. One of the newest techniques for link prediction is embedding methods, which are used to preparing the feature vector for each the nonexisting links in the network. In this paper, we introduce a novel approach to supervised link prediction based on feature embedding methods in order to achieve better performance. Our contribution considers a set of embedding methods as the feature vector for training the machine learning classifiers. The main focus of this work is to investigate the relevance of different feature embedding methods to improve the performance of the supervised link prediction models. The experimental results on some real-world temporal networks revealed satisfactory results, which encourage us for further analysis. Moreover, the use of feature embedding methods will provide better performance in this regard.

Heterogeneous Social network shape ability and willingness of rural residents to repay loans in China

This paper studies the impact and mechanism of heterogeneous social networks on credit repayment ability and willingness among Chinese rural residents. Based on the biprobit model with partial observability, we find a complementary relationship between repayment willingness and ability, and the heterogeneous social network effect differs with its nature. For repayment ability, bridging and bridged-linking social networks have positive effects whereas bonding and bond-linking social networks have insignificant effects. For repayment willingness, bonding and bond-linking social networks have positive effects whereas bridging and bridged-linking social networks have insignificant effect. Exploring mechanisms suggest that heterogeneous social networks affect repayment ability by alleviating income uncertainty, enhancing resident assets, and sharing risks; they shape repayment willingness through supervision, reputation, and punishment. Finally, the promoting effect of bonding and bond-linking networks on repayment willingness is enhanced by the construction of formal credit systems and the improvement of the informal credit climate.