Formation and Computation of Relationship in Complex Social Networks

Real social networks usually have some structural features of the complex networks, such as community structure, the scale-free degree distribution, clustering, small world network, dynamic evolution and so on. A new community detecting algorithm for directed and weighted social networks is proposed in this paper. Due to the use of more reference information, the accuracy of the algorithm is better than some of the typical detecting algorithms. And because of the use of heap structure and multi-task modular architecture, the algorithm also got a high computational efficiency than other algorithms. The effectiveness and efficiency of the algorithm is validated by experiments on real social networks. Based on the theories and models of complex networks, the features of the real large social networks are analyzed.

Each social network can be modeled as a graph G = [V, E] in which V is a vertex representing a person in this social network, and E is an edge representing the existence of a relationship between two individuals. The social infrastructure with size m is known as the Km group in the social network G with m individual. In other words, in a Km, each person knows other individuals and is in touch with them. The present study aims at developing a method for optimizing interpersonal communication in the social network using a simple cellular automaton algorithm. The experimental results obtained from both simulated social network and two real social networks were analyzed. The findings revealed that the proposed method has the potential to considerably reduce not only the number of colors assigned but also the running time of the program.

Collaborative filtering (CF)-based recommendation systems that rely on user-item history interactions often suffer from the data sparsity problem. Social-based recommendation methods have become one of the successful methods to address this problem. However, few works have focused on the sparsity problem of social data. As real-world social networks are usually sparse, the observed relationships in social networks can only represent a limited part of a person's real social network. The sparse social data will degrade the performance of the existing social-based algorithms. Also, the influence of a user's friends on their friends is dynamic: even the same friend may impact the target user in different decision-making processes. It is difficult for an the end-to-end deep learning-based model to provide underlying reasons for the recommendation results. To this end, we propose a novel deep learning-based model to extract useful missing links as auxiliary social information to enrich the users' features for item recommendation. The framework is composed of two major components: a missing links identifier module that generates useful social links from a heterogeneous information network to enrich the user's social profile and enhance the social-based recommendation model, and an attention-based recommendation module that assigns different scores for each friend with regard to different candidate items to adaptively evaluate the quality of different social links. An attention-based fusion strategy is proposed to improve the interpretability of the recommendation system by assigning non-uniform weight to different factors. Extensive experiments on three published datasets show that our proposed method achieves better performance than other state-of-the-art methods.

Some of the nodes of complex social networks may support for a given proposal, while the rest of the nodes may be against the given proposal. Even though all the nodes support for or are against the given proposal, the decision certitudes of individual nodes may be different. In this case, the steady state values of the decision certitudes of the majority of the nodes are either higher than or lower than a threshold value. Deriving the near consensus property is a key to the analysis of the behaviors of complex social networks. So far, no result on the behaviors of the complex social networks satisfying the near consensus property has been reported. Hence, it is useful to extend the definition of the exact consensus property to that of a near consensus property and investigate the behaviors of the complex social networks satisfying the near consensus property. This paper extends the definition of exact consensus complex social networks to that of near consensus complex social networks. For complex linear social networks, this paper investigates the relationships among the vectors representing the steady state values of the decision certitudes of the nodes, the influence weight matrix and the set of vectors representing the initial state values of the decision certitudes of the nodes under a given near consensus specification. The above analysis is based on the Eigen theory. For complex nonlinear social networks with certain types of nonlinearities, the relationship between the influence weight matrix and the vectors representing the steady state values of the decision certitudes of the nodes is studied. When a complex nonlinear social network does not achieve the exact consensus property, the optimal near consensus condition that the complex social network can achieve is derived. This problem is formulated as an optimization problem. The total number of nodes that the decision certitudes of the nodes are either higher than or lower than a threshold value is maximized subject to the corresponding near consensus specification. The optimization problem is a nonsmooth optimization problem. The nonsmooth constraints are first approximated by smooth constraints. Then, the approximated optimization problem is solved via a conventional smooth optimization approach. Computer numerical simulation results as well as the comparisons of the behaviors of complex nonlinear social networks to those of the complex linear social networks are presented. The obtained results demonstrate that some complex social networks can satisfy the near consensus property but not the exact consensus property. Also, the conditions for the near consensus property are dependent on the types of nonlinearities, the influence weight matrix and the vectors representing the initial state values of the decision certitudes of the nodes.

Toward link prediction in directed social networks based on common interest and local community

A novel negative feedback information dissemination model based on online social network

Various networks exist in the world today including biological, social, information, and communication networks with the Internet as the largest network of all. One salient structural feature of these networks is the formation of groups or communities of vertices that tend to be more connected to each other within the same group than to those outside. Therefore, the detection of these communities is a topic of great interest and importance in many applications and different algorithms including label propagation have been developed for such purpose. Speaker-listener label propagation algorithm (SLPA) enjoys almost linear time complexity, so desirable in dealing with large networks. As an extension of SLPA, this study presented a novel weighted label propagation algorithm (WLPA), which was tested on four real world social networks with known community structures including the famous Zachary's karate club network. Wilcoxon tests on the communities found in the karate club network by WLPA demonstrated an improved statistical significance over SLPA. Withthehelp of Wilcoxon tests again, we were able to determine the best possible formation of two communities in this network relative to the ground truth partition, which could be used as a new benchmark for assessing community detection algorithms. Finally WLPA predicted better communities than SLPA in two of the three additional real social networks, when compared to the ground truth.

Evolution of cooperation is a fundamental question of socio-biology. Intrinsic factors like kinship play an important role in cooperation among selfish individuals. External factors like uncertainty and the structure of the social interaction network also contribute significantly to the evolution of cooperation. Here I use agent-based simulations to generate artificial social networks. I show that some of these networks have similar scale-free structure as real social networks. The analysis shows that having agents with memory and with the ability to share their memory through gossiping does not have a significant effect on the scale-free nature of simulated social networks. However the presence of high uncertainty in the cooperation games played by the agents is required for the generation of scale-free social interaction networks.Keywordsagent-based modelingcooperationevolutionnetwork analysissimulationuncertainty

Today's social networks present multiplex and heterogeneous attributes, and they are becoming more and more difficult to grasp the unique attributes and structures of the network from the whole network structures. The attribute factors such as Degree Centrality and Closeness Centrality are inadequate in the study of social networks if they are not modified and taken fully into account the real characteristics. Although the evolution of a social network is a macroscopic process, the state of each node at different moments determines the trend of the overall state on the network. In order to better identify the key nodes in the social network and more really simulate information diffusion process, this paper proposes the method using WIE (Weighted Information Entropy) to measure node's influent force and importance degree based on the concept of information entropy in information theory, which carries higher-order local information of each node rather than only contains first-order local information like degree does. Different from the conventional SIR model, this paper realizes the dynamic calculation of the diffusion probability β in the improved SIR model according to node's local influence and weighted value. Finally, four kinds of real social networks are selected and analyzed. The experiment shows that the WIE measurement and the dynamically calculated diffusion probability can more truly reflect the information transmission in real social networks, and the node sets selected by WIE values have at least 10 percent improvement in diffusion efficiency.KeywordsSocial networksLocal influenceGlobal selectionSIR model

최근 소셜 네트워크 서비스가 급속히 증가함에 따라 많은 기업들과 사용자들은 자신들의 정보가 더욱 빠르게 확산되길 바란다. 이러한 소셜 네트워크에서의 정보 확산을 연구하기 위하여 많은 알고리즘들이 제안되었는데 그중 대표적인 알고리즘은 그리디 알고리즘(Greedy Algorithm)과 휴리스틱 알고리즘(Heuristic Algorithm)이다. 하지만 그리디 알고리즘의 경우 복잡성으로 인하여 실제 소셜 네트워크에 사용이 제한적이며, 기존 휴리스틱 기반의 메시지 전파 기법의 경우 균일한 소셜 네트워크 환경을 고려하였기 때문에 현재 소셜 네트워크의 특성을 반영한 연구가 요구된다. 이에 본 논문에서는 휴리스틱 알고리즘 기반의 정보 확산 확률과 노드 연결성을 고려한 정보 확산 능력 최대화 알고리즘을 제안하고, 실제 소셜 네트워크 데이터베이스를 이용하여 제안 알고리즘의 성능을 분석하였다. 그 결과 기존 알고리즘이 비해 더 많은 노드를 활성화 시킬 수 있어 정보 확산 능력을 향상시킬 수 있음을 확인하였다. Recently, with the proliferation of social network services, users and many companies hope that their information spread more faster. In order to study the information diffusion in the social networks, many algorithms such as greedy algorithm and heuristic algorithm have been proposed. However, the greedy algorithm is too complicated to use in real-life social network, and the heuristic algorithms have been studied under the uniform distribution of diffusion probability, which is different from the real social network property. In this paper, we propose an heuristic information diffusion maximization algorithm based on diffusion probability and node degree. For performance evaluation, we use real social network database, and it is verified that our proposed algorithm activates more active nodes than existing algorithms, which enables faster and wider information diffusion.

Online social networks (OSNs) have become increasingly relevant for studying social behavior and information diffusion. Nevertheless, they are limited by restricted access to real OSN data due to privacy, legal, and platform-related constraints. In response, synthetic social networks serve as a viable approach to support controlled experimentation, but current generators reproduce only topology and overlook attribute-driven homophily and semantic realism. This work proposes a homophily based algorithm that produces synthetic microblogging social networks such as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb{X}$</tex-math></inline-formula>. The model creates a social graph for a given number of users, integrating semantic affinity among user attributes, stochastic variation in link formation, triadic closure to foster clustering, and long-range connections to ensure global reachability. A systematic grid search is used to calibrate five hyperparameters (affinity strength, noise, closure probability, distant link probability, and candidate pool size) for reaching five structural values observed in real social networks (density, clustering coefficient, large connected component proportion, normalized shortest path, and modularity). The framework is validated by generating synthetic OSNs at four scales (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{3}$</tex-math></inline-formula>–<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{6}$</tex-math></inline-formula> nodes), and benchmarking them against a real-world Bluesky network comprising four million users. Comparative results show that the framework reliably reproduces the structural properties of the real network. Overall, the framework outperforms leading importance-sampling techniques and classical zero-shot generators applied to the same baseline. The generated graphs capture topological realism and yield attribute-driven communities that align with sociological expectations, providing a realistic, scalable testbed that liberates social researchers from relying on live digital platforms.

The conventional notion of community that favors a high ratio of internal edges to outbound edges becomes invalid when each vertex participates in multiple communities. Such a behavior is commonplace in social networks. The significant overlaps among communities make most existing community detection algorithms ineffective. The lack of effective and efficient tools resulted in very few empirical studies on large-scale detection and analyses of overlapping community structure in real social networks. We developed recently a scalable and accurate method called the Partial Community Merger Algorithm (PCMA) with linear complexity and demonstrated its effectiveness by analyzing two online social networks, Sina Weibo and Friendster, with 79.4 and 65.6 million vertices, respectively. Here, we report in-depth analyses of the 2.9 million communities detected by PCMA to uncover their complex overlapping structure. Each community usually overlaps with a significant number of other communities and has far more outbound edges than internal edges. Yet, the communities remain well separated from each other. Most vertices in a community are multi-membership vertices, and they can be at the core or the peripheral. Almost half of the entire network can be accounted for by an extremely dense network of communities, with the communities being the vertices and the overlaps being the edges. The empirical findings ask for rethinking the notion of community, especially the boundary of a community. Realizing that it is how the edges are organized that matters, the f-core is suggested as a suitable concept for overlapping community in social networks. The results shed new light on the understanding of overlapping community.

We live in a "small world," where two arbitrary people are likely connected by a short chain of intermediate friends. With scant information about a target individual, people can successively forward a message along such a chain. Experimental studies have verified this property in real social networks, and theoretical models have been advanced to explain it. However, existing theoretical models have not been shown to capture behavior in real-world social networks. Here, we introduce a richer model relating geography and social-network friendship, in which the probability of befriending a particular person is inversely proportional to the number of closer people. In a large social network, we show that one-third of the friendships are independent of geography and the remainder exhibit the proposed relationship. Further, we prove analytically that short chains can be discovered in every network exhibiting the relationship.

Social networks’ rapid information dissemination, massive user bases, and diverse content make them vulnerable to text steganography—a covert technique embedding secret messages into texts undetected, threatening personal privacy and network security. While text steganalysis serves as a critical defense mechanism, existing datasets for this task suffer from critical limitations including missing social graphs, insufficient text attributes, mismatched sample distributions, and limited data scale, hindering research progress. To address these gaps, this paper proposes a novel methodology for constructing a social network text steganalysis dataset via meta pathconstrained local group discovery and sample distribution dynamic regulation. It utilizes a local group discovery algorithm constrained by “user-tweet-hashtag” meta path to sample special user groups with potential covert communication intentions. In addition, a three-dimensional dynamic regulation strategy is designed to reshape the original tweets of the special users by adjusting the ratio, type, and distribution of steganographic texts, simulating complex and diverse covert communication patterns. Finally, a dataset is constructed with rich social graph information, namely SN-stego. It conforms to the characteristics of text fragmentation and steganography sparsity in real social networks, and simulates various social network text steganography analysis scenarios with complex and diverse sample distributions. Statistical analyses and empirical evaluations demonstrate that SN-stego exhibits substantial advancements in data scale, entity diversity, and scenario adaptability. The proposed method provides solid technical support for expanding and deepening the research on text steganalysis in social networks.

An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product’s information so that the benefit gained exceeds a predetermined threshold. Even though, marketing strategies exhibit different in several realistic scenarios due to market dependence or budget constraints. As a consequence, picking a seed set for a specific threshold is not enough to come up with an effective solution. To address the disadvantages of previous works with a new approach, we study the Multiple Benefit Thresholds (MBT), a generalized version of the IT problem, as a result of this phenomenon. Given a social network that is subjected to information distribution and a set of thresholds, T={T1,T2,…,Tk},Ti&gt;0, the issue aims to seek the seed sets S1,S2,…,Sk with the lowest possible cost so that the benefit achieved from the influence process is at the very least T1,T2,…,Tk, respectively. The main challenges of this problem are a #NP-hard problem and the estimation of the objective function #P-Hard under traditional information propagation models. In addition, adapting the exist algorithms many times to different thresholds can lead to large computational costs. To address the abovementioned challenges, we introduced Efficient Sampling for Selecting Multiple Seed Sets, an efficient technique with theoretical guarantees (ESSM). At the core of our algorithm, we developed a novel algorithmic framework that (1) can use the solution to a smaller threshold to find that of larger ones and (2) can leverage existing samples with the current solution to find that of larger ones. The extensive experiments on several real social networks were conducted in order to show the effectiveness and performance of our algorithm compared with current ones. The results indicated that our algorithm outperformed other state-of-the-art ones in terms of both the total cost and running time.