scholarly journals Search for dynamical origin of social networks

2016 ◽  
Author(s):  
Michail Zak
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Power Law ◽  
Liouville Equation ◽  
Quantum Algorithms ◽  
Intelligent Agent ◽  
Second Law Of Thermodynamics ◽  
Complete Problems ◽  
Proposed Model ◽  
Physical Particle

The challenge of this work is to re-define the concept of intelligent agent as a building block of social networks by presenting it as a physical particle with additional non-Newtonian properties. The proposed model of an intelligent agent described by a system of ODE coupled with their Liouville equation has been introduced and discussed. Following the Madelung equation that belongs to this class, non-Newtonian properties such as superposition, entanglement, and probability interference typical for quantum systems have been described. Special attention was paid to the capability to violate the second law of thermodynamics, which makes these systems neither Newtonian, nor quantum. It has been shown that the proposed model can be linked to mathematical models of livings as well as to models of AI. The model is presented in two modifications. The first one is illustrated by the discovery of a stochastic attractor approached by the social network; as an application, it was demonstrated that any statistics can be represented by an attractor of the solution to the corresponding system of ODE coupled with its Liouville equation. It was emphasized that evolution to the attractor reveals possible micro-mechanisms driving random events to the final distribution of the corresponding statistical law. Special attention is concentrated upon the power law and its dynamical interpretation: it is demonstrated that the underlying micro- dynamics supports a “violent reputation” of the power-law statistics. The second modification of the model of social network associated with a decision-making process and applied to solution of NP-complete problems known as being unsolvable neither by classical nor by quantum algorithms. The approach is illustrated by solving a search in unsorted database in polynomial time by resonance between external force representing the address of a required item and the response representing the location of this item.

scholarly journals Marketing and social networks: a criterion for detecting opinion leaders

2017 ◽  
Vol 26 (3) ◽  
pp. 347-366 ◽  
Author(s):  
Arnaldo Mario Litterio ◽  
Esteban Alberto Nantes ◽  
Juan Manuel Larrosa ◽  
Liliana Julia Gómez
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Semantic Analysis ◽  
Point Of View ◽  
Eigenvector Centrality ◽  
Content Type ◽  
Sporting Event ◽  
Centrality Metrics ◽  
Proposed Model

Purpose The purpose of this paper is to use the practical application of tools provided by social network theory for the detection of potential influencers from the point of view of marketing within online communities. It proposes a method to detect significant actors based on centrality metrics. Design/methodology/approach A matrix is proposed for the classification of the individuals that integrate a social network based on the combination of eigenvector centrality and betweenness centrality. The model is tested on a Facebook fan page for a sporting event. NodeXL is used to extract and analyze information. Semantic analysis and agent-based simulation are used to test the model. Findings The proposed model is effective in detecting actors with the potential to efficiently spread a message in relation to the rest of the community, which is achieved from their position within the network. Social network analysis (SNA) and the proposed model, in particular, are useful to detect subgroups of components with particular characteristics that are not evident from other analysis methods. Originality/value This paper approaches the application of SNA to online social communities from an empirical and experimental perspective. Its originality lies in combining information from two individual metrics to understand the phenomenon of influence. Online social networks are gaining relevance and the literature that exists in relation to this subject is still fragmented and incipient. This paper contributes to a better understanding of this phenomenon of networks and the development of better tools to manage it through the proposal of a novel method.

scholarly journals Image Based Classification of Rumor Information from the Social Network Platform

2021 ◽  
Vol 38 (5) ◽  
pp. 1413-1421
Author(s):  
Vallamchetty Sreenivasulu ◽  
Mohammed Abdul Wajeed
Keyword(s):  
Social Networks ◽  
Social Network ◽  
End Users ◽  
Web Based ◽  
Suggested Approach ◽  
Text Input ◽  
Social Network Data ◽  
Proposed Model ◽  
Spam Filters ◽  
Network Platform

Spam emails based on images readily evade text-based spam email filters. More and more spammers are adopting the technology. The essence of email is necessary in order to recognize image content. Web-based social networking is a method of communication between the information owner and end users for online exchanges that use social network data in the form of images and text. Nowadays, information is passed on to users in shorter time using social networks, and the spread of fraudulent material on social networks has become a major issue. It is critical to assess and decide which features the filters require to combat spammers. Spammers also insert text into photographs, causing text filters to fail. The detection of visual garbage material has become a hotspot study on spam filters on the Internet. The suggested approach includes a supplementary detection engine that uses visuals as well as text input. This paper proposed a system for the assessment of information, the detection of information on fraud-based mails and the avoidance of distribution to end users for the purpose of enhancing data protection and preventing safety problems. The proposed model utilizes Machine Learning and Convolutional Neural Network (CNN) methods to recognize and prevent fraud information being transmitted to end users.

scholarly journals Complex Neutrosophic Hypergraphs: New Social Network Models

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 234 ◽  
Author(s):  
Anam Luqman ◽  
Muhammad Akram ◽  
Florentin Smarandache
Keyword(s):  
Social Networks ◽  
Sustainable Development ◽  
Social Network ◽  
Complex Systems ◽  
Network Models ◽  
Neutrosophic Set ◽  
Neutrosophic Sets ◽  
Score Functions ◽  
Proposed Model ◽  
Complex Valued

A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models.

ANALYSIS OF COGNITIVE SOCIAL AND KNOWLEDGE NETWORKS FROM ELECTRONIC COMMUNICATION

2007 ◽  
Vol 01 (01) ◽  
pp. 87-120 ◽  
Author(s):  
NISHITH PATHAK ◽  
SANDEEP MANE ◽  
JAIDEEP SRIVASTAVA
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Electronic Communication ◽  
Knowledge Networks ◽  
Knowledge Network ◽  
Cognitive Network ◽  
Research Directions ◽  
Proposed Model ◽  
Cognitive Knowledge ◽  
New Research

This paper explains the classical social network analysis and discusses how computer networks effect a shift in constructing social networks. The paper then concentrates on analyzing cognitive aspects of a social network, explaining a simple but scalable approach for modeling a socio-cognitive network. Novel measures using such a socio-cognitive network model are defined and applications of such measures to extract useful information is illustrated on the Enron email dataset. The paper then describes a Dempster-Schafer theory based approach towards modeling a cognitive knowledge network and uses the Enron email dataset to illustrate how the proposed model can be used to capture actors' perceptions in a knowledge network. The paper concludes with a summary of the proposed models and a discussion on new research directions that can arise due to such cognitive analyses of electronic communication data.

scholarly journals Dimensionality of Social Networks Using Motifs and Eigenvalues

2021 ◽  
Author(s):  
Anthony Bonato ◽  
David F. Gleich ◽  
Myunghwan Kim ◽  
Dieter Mitsche ◽  
Paweł Prałat ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Model ◽  
Power Law ◽  
Metric Space ◽  
Real World ◽  
Eigenvalue Distribution

We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedIn. Further, we employ two different methods for confirming the hypothesis: the first uses the distribution of motif counts, and the second exploits the eigenvalue distribution.

Design of a Novel Query System for Social Network

2019 ◽  
Vol 12 (2) ◽  
pp. 175-193
Author(s):  
Charu Virmani ◽  
Dimple Juneja ◽  
Anuradha Pillai
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Vital Role ◽  
Agglomerative Clustering ◽  
User Intention ◽  
User Query ◽  
Proposed Model ◽  
Hierarchical Agglomerative Clustering ◽  
Query System

User intention and nature of network plays a vital role towards the quality of response received as the result of any user query. Therefore, the need of system understanding the user's intent and network dynamism as well is highly apparent. The proposed query processing and analysing system (QPAS) for social networks is based on extracting user's intent from various social networks using existing NLP techniques. It fetches the information and further employs hybrid ensemble k-means hierarchical agglomerative clustering (HEKHAC) and modified Bitonic sort to improve the responses. The proposed approach offers an edge over other mechanisms as it not only retrieves more user-centric results as compared to traditional way of keyword-based searching but also in timely manner as well. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a noteworthy revolution scoring up to precision and recall respectively.

scholarly journals Dimensionality of Social Networks Using Motifs and Eigenvalues

2021 ◽  
Author(s):  
Anthony Bonato ◽  
David F. Gleich ◽  
Myunghwan Kim ◽  
Dieter Mitsche ◽  
Paweł Prałat ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Model ◽  
Power Law ◽  
Metric Space ◽  
Real World ◽  
Eigenvalue Distribution

We consider the dimensionality of social networks, and develop experiments aimed at predicting that dimension. We find that a social network model with nodes and links sampled from an m-dimensional metric space with power-law distributed influence regions best fits samples from real-world networks when m scales logarithmically with the number of nodes of the network. This supports a logarithmic dimension hypothesis, and we provide evidence with two different social networks, Facebook and LinkedIn. Further, we employ two different methods for confirming the hypothesis: the first uses the distribution of motif counts, and the second exploits the eigenvalue distribution.

Investigating the Properties of a Social Bookmarking and Tagging Network

2010 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Ralitsa Angelova ◽  
Marek Lipczak ◽  
Evangelos Milios ◽  
Pawel Pralat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Random Graphs ◽  
Power Law ◽  
Information Dissemination ◽  
Collaborative Tagging ◽  
Social Bookmarking ◽  
Primary Means ◽  
Common Interests ◽  
Law Degree

Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us3 is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links that make del.icio.us a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly “similar” users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. The authors investigate the question whether friends have common interests, they gain additional insights on the strategies that users use to assign tags to their bookmarks, and they demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution.

Investigating the Properties of a Social Bookmarking and Tagging Network

2011 ◽  
pp. 1-16
Author(s):  
Ralitsa Angelova ◽  
Marek Lipczak ◽  
Evangelos Milios ◽  
Pawel Pralat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Random Graphs ◽  
Power Law ◽  
Degree Distribution ◽  
Information Dissemination ◽  
Social Bookmarking ◽  
Primary Means ◽  
Common Interests ◽  
Law Degree

Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us3 is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make del.icio.us a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly “similar” users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. The authors investigate the question whether friends have common interests, they gain additional insights on the strategies that users use to assign tags to their bookmarks, and they demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution.

Modelling information diffusion based on non-dominated friends in social networks

2016 ◽  
Vol 43 (6) ◽  
pp. 801-815 ◽  
Author(s):  
Niloofar Mozafari ◽  
Ali Hamzeh ◽  
Sattar Hashemi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Diffusion Model ◽  
Information Diffusion ◽  
Information Dissemination ◽  
Single Factor ◽  
Proposed Model ◽  
The Social ◽  
Diffusion Pattern ◽  
Information Diffusion Model

In recent years, social networks have played a strong role in diffusing information among people all around the globe. Therefore, the ability to analyse the diffusion pattern is essential. A diffusion model can identify the information dissemination pattern in a social network. One of the most important components of a diffusion model is information perception which determines the source each node receives its information from. Previous studies have assumed information perception to be just based on a single factor, that is, each individual receives information from their friend with the highest amount of information, whereas in reality, there exist other factors, such as trust, that affect the decision of people for selecting the friend who would supply information. These factors might be in conflict with each other, and modelling diffusion process with respect to a single factor can give rise to unacceptable results with respect to the other factors. In this article, we propose a novel information diffusion model based on non-dominated friends (IDNDF). Non-dominated friends are a set of friends of a node for whom there is no friend better than them in the set based on all considered factors, considering different factors simultaneously significantly enhance the proposed information diffusion model. Moreover, our model gives a chance to all non-dominated friends to be selected. Also, IDNDF allows having partial knowledge by each node of the social network. Finally, IDNDF is applicable to different types of data, including well-known real social networks like Epinions, WikiPedia, Advogato and so on. Extensive experiments are performed to assess the performance of the proposed model. The results show the efficiency of the IDNDF in diffusion of information in varieties of social networks.

Sign in / Sign up

Export Citation Format

Share Document