Building Complex Network Similar to Facebook

2014 ◽  
Vol 513-517 ◽  
pp. 909-913
Author(s):  
Dong Wei Guo ◽  
Xiang Yan Meng ◽  
Cai Fang Hou
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Clustering Coefficient ◽  
Network Characteristics ◽  
Modeling Methods ◽  
Community Strength ◽  
Strength Based

Social networks have been developed rapidly, especially for Facebook which is very popular with 10 billion users. It is a considerable significant job to build complex network similar to Facebook. There are many modeling methods of complex networks but which cant describe characteristics similar to Facebook. This paper provide a building method of complex networks with tunable clustering coefficient and community strength based on BA network model to imitate Facebook. The strategies of edge adding based on link-via-triangular, link-via-BA and link-via-type are used to build a complex network with tunable clustering coefficient and community strength. Under different parameters, statistical properties of the complex network model are analyzed. The differences and similarities are studied among complex network model proposed by this paper and real social network on Facebook. It is found that the network characteristics of the network model and real social network on Facebook are similar under some specific parameters. It is proved that the building method of complex networks is feasible.

scholarly journals Group Relationship Mining of College Students Based on Predictive Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Huazhang Liu
Keyword(s):  
College Students ◽  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Link Prediction ◽  
Rapid Development ◽  
Combination Method ◽  
Prediction Algorithm ◽  
The Social ◽  
Random Block

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

scholarly journals EXPANDING SOCIAL TIES AS A GOAL IN LATER LIFE: THE ROLE OF SOCIAL NETWORK CHARACTERISTICS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S175-S175
Author(s):  
Danielle Oleskiewicz ◽  
Karen Rook
Keyword(s):  
Older Adults ◽  
Social Networks ◽  
Social Network ◽  
Social Ties ◽  
Later Life ◽  
Well Being ◽  
Support Provision ◽  
Network Characteristics ◽  
Network Composition

Abstract Older adults often winnow their social ties to focus on emotionally rewarding ties (Charles & Carstensen, 2010). Some older adults, however, have small social networks that preclude much winnowing or aversive social ties from which disengagement is difficult. These individuals might be motivated to expand, rather than contract, their social ties. The current study sought to extend knowledge regarding potential links between social network characteristics and older adults’ interest, effort, and success in creating new social ties. We expected that small social networks and negative social ties might motivate interest and effort directed toward forming new social ties but that positive social ties might foster success in efforts to form new ties. In-person interviews were conducted with participants (N = 351, Mean age = 74.16) in a larger study of older adults’ social networks and well-being. The interviews assessed participants’ social networks, as well as their interest, effort, and success in making new social ties. Participants’ social network composition, rather than size, was associated with greater motivation to establish new social ties. Negative social ties were associated with greater interest and effort directed toward forming new social ties. Positive social ties were related to greater success (due, in part, to their support provision) and, unexpectedly, were also related to greater interest and effort directed toward forming new ties. Older adults sometimes seek to expand, rather than contract, their social ties, and characteristics of their social networks appear to play a role in fueling and influencing the success of such efforts.

scholarly journals Synchronization in a Novel Local-World Dynamical Network Model

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianeng Tang ◽  
Peizhong Liu
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Network Model ◽  
Optimal Growth ◽  
Dynamical Networks ◽  
Dynamical Network ◽  
Network Research ◽  
Topology Model ◽  
The Relationship ◽  
Random Failures

Advances in complex network research have recently stimulated increasing interests in understanding the relationship between the topology and dynamics of complex networks. In the paper, we study the synchronizability of a class of local-world dynamical networks. Then, we have proposed a local-world synchronization-optimal growth topology model. Compared with the local-world evolving network model, it exhibits a stronger synchronizability. We also investigate the robustness of the synchronizability with respect to random failures and the fragility of the synchronizability with specific removal of nodes.

Attachment in Social Networks: Toward an Evolutionary Social Network Model

2005 ◽  
Vol 48 (1-2) ◽  
pp. 85-88 ◽  
Author(s):  
Marinus H. van Ijzendoorn
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Model

Network model of bilateral power markets based on complex networks

2014 ◽  
Vol 28 (22) ◽  
pp. 1450144 ◽  
Author(s):  
Yang Wu ◽  
Junyong Liu ◽  
Furong Li ◽  
Zhanxin Yan ◽  
Li Zhang
Keyword(s):  
Complex Networks ◽  
Network Model ◽  
Simulation Analysis ◽  
Electric Power Industry ◽  
Clustering Coefficient ◽  
Power Markets ◽  
Cascading Failure ◽  
Topological Stability ◽  
Market Organization ◽  
Topological Robustness

The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.

scholarly journals Social Networks, Community Engagement, and Cognitive Impairment among Community-Dwelling Chinese Older Adults

2019 ◽  
Vol 9 (3) ◽  
pp. 330-337
Author(s):  
Jinlei Li ◽  
Zijuan Wang ◽  
Zhiwei Lian ◽  
Zhikai Zhu ◽  
Yuanli Liu
Keyword(s):  
Older Adults ◽  
Social Networks ◽  
Cognitive Impairment ◽  
Social Network ◽  
Cognitive Function ◽  
Community Engagement ◽  
Community Dwelling ◽  
Chinese Older Adults ◽  
Network Characteristics ◽  
Community Activities

Aims: To examine the association of social networks and community engagement with cognitive impairment among community-dwelling Chinese older adults. Methods: From November 2017 to May 2018, we selected 1,115 elderly individuals from 3 Chinese communities (Beijing, Hefei, and Lanzhou) using a random-cluster sampling method, and recorded data on demographics, social network characteristics, community activities, and cognitive function. The odds ratios (ORs) of these associations were adjusted for potential confounders in logistic regression models. Results: The prevalence of cognitive impairment was 25.7% (n = 287). An adequate social network (OR 0.55; 95% confidence interval [CI] 0.33–0.91) and enough social support from friends (OR 0.43; 95% CI 0.29–0.62) were negatively associated with cognitive impairment. Family support was not significantly associated with cognitive impairment (OR 0.64; 95% CI 0.34–1.21). Taking part in elderly group travel, communication with others using WeChat, and community activities such as Tai Chi and walking together were negatively associated with cognitive impairment. Conclusion: Social network characteristics and community engagement were found to be related to cognitive function among community-dwelling Chinese elderly adults.

A novel genetic algorithm-based improvement model for online communities and trust networks

2021 ◽  
Vol 40 (1) ◽  
pp. 1597-1608
Author(s):  
Ilker Bekmezci ◽  
Murat Ermis ◽  
Egemen Berki Cimen
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Online Communities ◽  
Real Life ◽  
Statistical Properties ◽  
Clustering Coefficient ◽  
Data Sets ◽  
Life Data ◽  
Real Life Data

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks.

Hadoop-Based Distributed K-Shell Decomposition for Social Networks

2018 ◽  
pp. 125-145
Author(s):  
Katerina Pechlivanidou ◽  
Dimitrios Katsaros ◽  
Leandros Tassiulas
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Complex Network ◽  
Online Social Networks ◽  
Distributed Algorithm ◽  
Data Centers ◽  
Large Data ◽  
Complex Network Analysis ◽  
Centrality Analysis

Complex network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a network is a technique that has been used for centrality analysis, for communities' discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run, i.e., large data centers, makes none of the existing algorithms appropriate for the decomposition of graphs into shells. In this article, we develop for a distributed algorithm based on MapReduce for the k-shell decomposition of a graph. We furthermore, provide an implementation and assessment of the algorithm using real social network datasets. We analyze the tradeoffs and speedup of the proposed algorithm and conclude for its virtues and shortcomings.

Who Belongs to Me? Social Relationship and Personality Characteristics in the Transition to Young Adulthood

2014 ◽  
Vol 28 (6) ◽  
pp. 586-603 ◽  
Author(s):  
Jenny Wagner ◽  
Oliver Lüdtke ◽  
Brent W. Roberts ◽  
Ulrich Trautwein
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Young Adulthood ◽  
Social Relationship ◽  
Multilevel Models ◽  
Network Size ◽  
Big Five Personality ◽  
Personality Psychology ◽  
Emotional Closeness ◽  
Network Characteristics

Not much is known about how social network characteristics change in the transition out of school and what role Big Five personality plays in this context. The aim of this paper was twofold. First, we explored changes in social network and relationship characteristics across the transition out of secondary school. Second, we examined within–person and between–person effects of personality on these social network changes. Results based on a series of multilevel models to a longitudinal sample of 2287 young adults revealed four main findings. First, social networks increased in size, and this increase was mainly due to a larger number of nonkin. Stable social networks during the transition consisted mainly of family ties but were generally characterized by high closeness. Second, extraversion and openness consistently predicted network size, whereas agreeableness predicted network overlap. Third, increases in emotional closeness were found only for kin; closeness was generally lower for unstable relationships. Fourth, changes in emotional closeness were related to personality, particularly neuroticism, agreeableness, and conscientiousness for stable relationships; for unstable relationships, however, closeness was related to extraversion and openness. The article concludes by discussing the role of personality for social relationship development and the active moulding of social networks in young adulthood. Copyright © 2014 European Association of Personality Psychology

scholarly journals Construction of complex networks from time series based on the cross correlation interval

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 253-260 ◽  
Author(s):  
Chen Feng ◽  
Bo He
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Complex Network ◽  
Cross Correlation ◽  
Clustering Coefficient ◽  
Two Phase ◽  
Correlation Interval ◽  
The Cross ◽  
The Lorenz System ◽  
Dynamic States

AbstractIn this paper, a new approach to map time series into complex networks based on the cross correlation interval is proposed for the analysis of dynamic states of time series on different scales. In the proposed approach, a time series is divided into time series segments and each segment is reconstructed to a phase space defined as a node of the complex network. The cross correlation interval, which characterizes the degree of correlation between two phase spaces, is computed as the distance between the two nodes. The clustering coefficient and efficiency are used to determine an appropriate threshold for the construction of a complex network that can effectively describe the dynamic states of a complex system. In order to verify the efficiency of the proposed approach, complex networks are constructed for time series generated from the Lorenz system, for white Gaussian noise time series and for sea clutter time series. The experimental results have demonstrated that nodes in different communities represent different dynamic states . Therefore, the proposed approach can be used to uncover the dynamic characteristics of the complex systems.

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