The evolution and structure of social networks

2014 ◽  
Vol 2 (3) ◽  
pp. 326-340
Author(s):  
WHITMAN RICHARDS ◽  
NICHOLAS WORMALD
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Empirical Data ◽  
Large Scale ◽  
Preferential Attachment ◽  
The Other ◽  
Local Patterns ◽  
Global Patterns ◽  
Scale Properties ◽  
Empirical Networks

AbstractAs social networks evolve, new nodes are linked to the large-scale organization already in place. We show that the combination of two simple algorithms, one the Barabasi-Albert preferential attachment proposal and the other a neighbor attachment rule, successfully generate networks exhibiting both the local and global characteristics of empirical data on social network structures. Ideally, one might hope that some coarse features of this linking process and the form of the local patterns might enable the prediction of large-scale properties. We show that this is generally not the case. This might help explain the variety of local and global patterns in empirical networks.

scholarly journals Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

2012 ◽  
Vol 367 (1599) ◽  
pp. 2108-2118 ◽  
Author(s):  
Louise Barrett ◽  
S. Peter Henzi ◽  
David Lusseau
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Life ◽  
The Other ◽  
Feedback Process ◽  
Cognitive Evolution ◽  
Systems Approaches ◽  
Entropy Measures ◽  
Free Ranging ◽  
Source Of Information

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

Il radicamento di una comunitŕ di Xoraxanč Romŕ a Roma

2009 ◽  
pp. 67-84
Author(s):  
Marco Solimene
Keyword(s):  
Social Networks ◽  
Social Network ◽  
The Other ◽  
Present Contribution ◽  
Other Hand ◽  
The One ◽  
The City

- The present contribution examines the rootedness of a community of xoraxané romá in the city of Rome; rather than simply the continuity of presence in a specific territory, under consideration is the development and maintenance of social networks with the Roman population, specifically in the territories romá reside and/or work in. Further on, the paper describes how rootedness may be conjugated with some forms of mobility: on the one hand, the continuity in specific areas (of work and in some cases of residence), can be maintained through practices of urban circulation; on the other hand, especially when mobility turns on national and transnational scale, the presence - although mobile and changing - of romá who belong to the same social network, spread among different territories, enables singular domestic units to maintain, despite mobility, a continuity with several non-rom realities.

A method of hidden messages embedding in graphlike structures of a social network

2018 ◽  
Author(s):  
И.В. Нечта
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Main Idea ◽  
The Other ◽  
Secret Message ◽  
Automatic Registration ◽  
Connected Component ◽  
Single Node ◽  
Empty Container ◽  
First Time

Предложен новый метод передачи скрытых сообщений в социальных сетях на примере сети “Вконтакте”, позволяющий через структуру графа друзей пользователя внедрять секретные сообщения. Получены количественные оценки объема внедряемого сообщения в графы различного размера. Показана необходимость добавления избыточности во внедряемое сообщение. Представленный метод позволяет использовать другие графоподобные структуры социальной сети для внедрения скрытых сообщений. Purpose. This article addresses the construction of a new method for transmission of hidden messages in social networks. Methodology. The research employs methods of information theory, probability theory and mathematical statistics The Shannon entropy is used as the statistics for the analysis of an embedded message. Findings. The author proposed using the graphical structures of social networks as a container for the secret message transmission for the first time. As an example, the popular Vkontakte network is considered. The main idea of the method involves using the structure of the user’s friends graph to embed a secret message. Based on the available vertices (friends’ accounts), a complete graph is constructed, and its edges are enumerated. Each edge of the graph corresponds to one bit of the message being embedded: the bit is “1”, if the edge is present in the graph (one account in friends of the other), the bit is “0” if the edge is missing. To transfer the graph from one person to another, a key vertex is used. The specified vertex is connected by an edge with each connected component of the graph, which allows the graph to be transmitted using a single node and take into account all the vertices (including isolated ones). When retrieving a message, the key vertex and the edges connected to it are not considered. Conclusions. During the experimental research, it was shown that messages extracted from an empty container differ from the encrypted message by the probability distribution of bits. The necessity of adding redundancy to transmitted secret messages is shown with the purpose of “leveling” the statistical properties of an empty and filled container. The results of the experiment have showed that this method of steganography allows embedding a large amount of information into various social network structures represented in the form of a graph. It was noted in the paper that potentially “narrow” place of the algorithm is registration of new accounts. The restrictions imposed by the administration of some social networks for security purposes do not always allow automatic registration of new accounts, which makes the process of message embedding more difficult.

Community Detection in Large-Scale Social Networks

2018 ◽  
pp. 189-206 ◽  
Author(s):  
S Rao Chintalapudi ◽  
M. H. M. Krishna Prasad
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Large Scale ◽  
The Other ◽  
Edge Density ◽  
Challenging Problem ◽  
Overlapping Community Detection ◽  
Clustering Problem ◽  
Detection Algorithms ◽  
Overlapping Community

Community Structure is one of the most important properties of social networks. Detecting such structures is a challenging problem in the area of social network analysis. Community is a collection of nodes with dense connections than with the rest of the network. It is similar to clustering problem in which intra cluster edge density is more than the inter cluster edge density. Community detection algorithms are of two categories, one is disjoint community detection, in which a node can be a member of only one community at most, and the other is overlapping community detection, in which a node can be a member of more than one community. This chapter reviews the state-of-the-art disjoint and overlapping community detection algorithms. Also, the measures needed to evaluate a disjoint and overlapping community detection algorithms are discussed in detail.

Visualizing Co-Authorship Social Networks and Collaboration Recommendations With CNARe

2018 ◽  
pp. 173-188
Author(s):  
Michele A. Brandão ◽  
Matheus A. Diniz ◽  
Guilherme A. de Sousa ◽  
Mirella M. Moro
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Online Tool ◽  
Strength Of Ties ◽  
E Learning

Studies have analyzed social networks considering a plethora of metrics for different goals, from improving e-learning to recommend people and things. Here, we focus on large-scale social networks defined by researchers and their common published articles, which form co-authorship social networks. Then, we introduce CNARe, an online tool that analyzes the networks and present recommendations of collaborations based on three different algorithms (Affin, CORALS and MVCWalker). Through visualizations and social networks metrics, CNARe also allows to investigate how the recommendations affect the co-authorship social networks, how researchers' networks are in a central and eagle-eye context, and how the strength of ties behaves in large co-authorship social networks. Furthermore, users can upload their own network in CNARe and make their own recommendation and social network analysis.

Extending Social Network Diagrams for Large Scale Collaboration

2012 ◽  
pp. 1-13
Author(s):  
I.T. Hawryszkiewycz
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Web 2.0 ◽  
Large Scale ◽  
Policy Planning ◽  
Web 2.0 Technologies ◽  
Enterprise Social Networks ◽  
Cloud Technologies ◽  
Common Vision ◽  
Network Diagrams

The chapter provides a way for modeling large scale collaboration using an extension to social network diagrams called enterprise social networks (ESNs). The chapter uses the ESN diagrams to describe activities in policy planning and uses these to define the services to be provided by cloud technologies to support large scale collaboration. This chapter describes collaboration by an architecture made up of communities each with a role to ensure that collaboration is sustainable. The architecture is based on the idea of an ensemble of communities all working to a common vision supported by services provided by the collaboration cloud using Web 2.0 technologies.

Large-scale dynamic social data representation for structure feature learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5253-5262
Author(s):  
Xiaoxian Zhang ◽  
Jianpei Zhang ◽  
Jing Yang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Dynamic Change ◽  
Feature Learning ◽  
Data Representation ◽  
Feature Representation ◽  
Small Scale ◽  
Dynamic Feature ◽  
Dynamic Social Networks

The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.

scholarly journals Private algorithms for the protected in social network search

2016 ◽  
Vol 113 (4) ◽  
pp. 913-918 ◽  
Author(s):  
Michael Kearns ◽  
Aaron Roth ◽  
Zhiwei Steven Wu ◽  
Grigory Yaroslavtsev
Keyword(s):  
Infectious Disease ◽  
Social Networks ◽  
Social Network ◽  
Data Privacy ◽  
Large Scale ◽  
Graph Search ◽  
Medical Testing ◽  
Background Checks ◽  
Network Search ◽  
Natural Variants

Motivated by tensions between data privacy for individual citizens and societal priorities such as counterterrorism and the containment of infectious disease, we introduce a computational model that distinguishes between parties for whom privacy is explicitly protected, and those for whom it is not (the targeted subpopulation). The goal is the development of algorithms that can effectively identify and take action upon members of the targeted subpopulation in a way that minimally compromises the privacy of the protected, while simultaneously limiting the expense of distinguishing members of the two groups via costly mechanisms such as surveillance, background checks, or medical testing. Within this framework, we provide provably privacy-preserving algorithms for targeted search in social networks. These algorithms are natural variants of common graph search methods, and ensure privacy for the protected by the careful injection of noise in the prioritization of potential targets. We validate the utility of our algorithms with extensive computational experiments on two large-scale social network datasets.

scholarly journals THE SOCIAL NETWORK OF CONTEMPORARY POPULAR MUSICIANS

2007 ◽  
Vol 17 (07) ◽  
pp. 2281-2288 ◽  
Author(s):  
JUYONG PARK ◽  
OSCAR CELMA ◽  
MARKUS KOPPENBERGER ◽  
PEDRO CANO ◽  
JAVIER M. BULDÚ
Keyword(s):  
Social Networks ◽  
Social Network ◽  
The Other ◽  
Collaboration Network ◽  
Perceived Similarity ◽  
Similarity Network ◽  
Self Organized ◽  
The Social ◽  
Information Database

In this paper, we analyze two social network datasets of contemporary musicians constructed from allmusic.com (AMG), a music and artists' information database: one is the collaboration network in which two musicians are connected if they have performed or produced an album together, and the other is the similarity network in which they are connected if they were musically similar according to the music experts. We find that, while both networks exhibit typical features of social networks such as high transitivity (clustering), we find that they differ significantly in some key network features such as the degree and the betweenness distributions. We believe that this highlights the fundamental differences in the construction mechanism (self-organized collaboration and human-perceived similarity) of the new networks.

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