Social network topology: a Bayesian approach

Network motifs are small subgraphs that reflect local network topology and were shown to be useful for creating profiles that reveal several properties of the network. In this work the motif analysis of the e-mail network of the Wroclaw University of Technology, consisting of over 4000 nodes was conducted. Temporal changes in the network structure during the period of 20 months were analysed and the correlations between global structural parameters of the network and motif distribution were found. These results are to be used in the development of methods dedicated for fast estimating of the properties of complex internet-based social networks.

Download Full-text

The Speed of Innovation Diffusion in Social Networks

Econometrica ◽

10.3982/ecta17007 ◽

2020 ◽

Vol 88 (2) ◽

pp. 569-594

Author(s):

Itai Arieli ◽

Yakov Babichenko ◽

Ron Peretz ◽

H. Peyton Young

Keyword(s):

Social Networks ◽

Social Network ◽

Waiting Time ◽

Network Topology ◽

Upper Bound ◽

Status Quo ◽

Arbitrary Size ◽

The Status ◽

Expected Waiting Time ◽

Regular Networks

New ways of doing things often get started through the actions of a few innovators, then diffuse rapidly as more and more people come into contact with prior adopters in their social network. Much of the literature focuses on the speed of diffusion as a function of the network topology. In practice, the topology may not be known with any precision, and it is constantly in flux as links are formed and severed. Here, we establish an upper bound on the expected waiting time until a given proportion of the population has adopted that holds independently of the network structure. Kreindler and Young (2014) demonstrated such a bound for regular networks when agents choose between two options: the innovation and the status quo. Our bound holds for directed and undirected networks of arbitrary size and degree distribution, and for multiple competing innovations with different payoffs.

Download Full-text

A MODEL FOR BRAND COMPETITION WITHIN A SOCIAL NETWORK

International Journal of Modern Physics C ◽

10.1142/s0129183110015981 ◽

2010 ◽

Vol 21 (12) ◽

pp. 1457-1467

Author(s):

R. HUERTA-QUINTANILLA ◽

E. CANTO-LUGO ◽

M. RODRÍGUEZ-ACHACH

Keyword(s):

Social Network ◽

Network Topology ◽

Decision Rules ◽

Nearest Neighbors ◽

Product Market ◽

Economic Benefits ◽

Model Parameters ◽

Agent Based ◽

Absorbing State ◽

Brand Competition

An agent-based model was built representing an economic environment in which m brands are competing for a product market. These agents represent companies that interact within a social network in which a certain agent persuades others to update or shift their brands; the brands of the products they are using. Decision rules were established that caused each agent to react according to the economic benefits it would receive; they updated/shifted only if it was beneficial. Each agent can have only one of the m possible brands, and she can interact with its two nearest neighbors and another set of agents which are chosen according to a particular set of rules in the network topology. An absorbing state was always reached in which a single brand monopolized the network (known as condensation). The condensation time varied as a function of model parameters is studied including an analysis of brand competition using different networks.

Download Full-text

Properties of a Social Network Topology of Livestock Movements to Slaughterhouse in Korea

Journal of Veterinary Clinics ◽

10.17555/jvc.2016.10.33.5.278 ◽

2016 ◽

Vol 33 (5) ◽

pp. 278

Author(s):

Hyuk Park ◽

Sunhak Bae ◽

Son-Il Pak

Keyword(s):

Social Network ◽

Network Topology

Download Full-text

The Algorithm of Link Prediction on Social Network

Mathematical Problems in Engineering ◽

10.1155/2013/125123 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 17

Author(s):

Liyan Dong ◽

Yongli Li ◽

Han Yin ◽

Huang Le ◽

Mao Rui

Keyword(s):

Social Network ◽

Network Topology ◽

Link Prediction ◽

Prediction Algorithm ◽

Similarity Function ◽

Information Network ◽

Global Information ◽

Data Set ◽

Prediction Algorithms ◽

Topology Information

At present, most link prediction algorithms are based on the similarity between two entities. Social network topology information is one of the main sources to design the similarity function between entities. But the existing link prediction algorithms do not apply the network topology information sufficiently. For lack of traditional link prediction algorithms, we propose two improved algorithms: CNGF algorithm based on local information and KatzGF algorithm based on global information network. For the defect of the stationary of social network, we also provide the link prediction algorithm based on nodes multiple attributes information. Finally, we verified these algorithms on DBLP data set, and the experimental results show that the performance of the improved algorithm is superior to that of the traditional link prediction algorithm.

Download Full-text