scholarly journals Dense and sparse vertex connectivity in networks

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Mehdi Djellabi ◽  
Bertrand Jouve ◽  
Frédéric Amblard
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Real Data ◽  
Data Sets ◽  
Vertex Connectivity ◽  
Local Interactions ◽  
Internal Organization ◽  
The Way

Abstract The different approaches developed to analyse the structure of complex networks have generated a large number of studies. In the field of social networks at least, studies mainly address the detection and analysis of communities. In this article, we challenge these approaches and focus on nodes that have meaningful local interactions able to identify the internal organization of communities or the way communities are assembled. We propose an algorithm, ItRich, to identify this type of nodes, based on the decomposition of a graph into successive, less and less dense, layers. Our method is tested on synthetic and real data sets and meshes well with other methods such as community detection or $k$-core decomposition.

COMMUNITY DETECTION IN SOCIAL NETWORKS EMPLOYING COMPONENT INDEPENDENCY

2009 ◽  
Vol 23 (17) ◽  
pp. 2089-2106 ◽  
Author(s):  
ZHONGMIN XIONG ◽  
WEI WANG
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Theoretical Analysis ◽  
Community Detection ◽  
Biological Networks ◽  
Real Data ◽  
Important Task ◽  
Underlying Structure ◽  
Data Sets ◽  
Detection Algorithms

Many networks, including social and biological networks, are naturally divided into communities. Community detection is an important task when discovering the underlying structure in networks. GN algorithm is one of the most influential detection algorithms based on betweenness scores of edges, but it is computationally costly, as all betweenness scores need to be repeatedly computed once an edge is removed. This paper presents an algorithm which is also based on betweenness scores but more than one edge can be removed when all betweenness scores have been computed. This method is motivated by the following considerations: many components, divided from networks, are independent of each other in their recalculation of betweenness scores and their split into smaller components. It is shown that this method is fast and effective through theoretical analysis and experiments with several real data sets, which have acted as test beds in many related works. Moreover, the version of this method with the minor adjustments allows for the discovery of the communities surrounding a given node without having to compute the full community structure of a graph.

scholarly journals COMPREHENSIVE ANALYSIS OF LINGUISTIC COMMUNICATION PATTERNS OF TOP RUSSIAN JOURNALISTS IN THE OFFICIAL ACCOUNTS OF SOCIAL PLATFORMS VKONTAKTE, FACEBOOK, INSTAGRAM, TWITTER

2021 ◽  
pp. 49-62
Author(s):  
Вероника Викторовна Катермина ◽  
Анна Александровна Гнедаш ◽  
Мария Витальевна Николаева
Keyword(s):  
Social Networks ◽  
Social Action ◽  
Presidential Address ◽  
Comprehensive Analysis ◽  
Communication Patterns ◽  
Data Sets ◽  
Linguistic Communication ◽  
The Social ◽  
Linguistic Patterns ◽  
The Way

В статье приводятся результаты комплексного анализа лингвистических паттернов коммуникации топовых российских журналистов в официальных аккаунтах социальных платформ ВКонтакте, Facebook, Instagram, Twitter. Целью данной статьи является изучение лингвистических паттернов, продуцируемых топовыми журналистами в своих онлайн-аккаунтах, способных задавать векторы восприятия политического контента, создаваемого главными лидерами государств, и приводящих к трансформации дискурсивных полей как в онлайн-, так и в офлайн-пространстве. Среднестатистический россиянин тратит почти половину дня на онлайн-взаимодействие, почти 50 % этого времени приходится на популярные социальные медиа, в том числе интернет-серфинг в среде официальных аккаунтов топовых журналистов. Потребление данных паттернов рядовыми пользователями / читателями, находящимися под «силовым» влиянием дискурсивного поля, становится определяющим фактором в процессе выработки и принятия индивидуальных / коллективных решений, реализация которых формирует то или иное социальное действие как в онлайн-, так и в офлайн-пространстве. Согласно данным мониторинга социальных медиа и СМИ компанией «Медиалогия», нами были выбраны аккаунты Алексея Венедиктова, Владимира Соловьева, Владимира Познера, Маргариты Симоньян и Ксении Собчак в ВКонтакте, Facebook, Instagram, Twitter. Эмпирической базой (дата-сеты) стали все посты, комментарии и ветки дискуссий, отражающие реакцию данных журналистов и общественности на Послание Президента РФ В. В. Путина Федеральному Собранию РФ от 15 января 2020 г. Дата-сеты были получены машинным методом сплошной выборки и подвергнуты комплексному анализу, включившему сетевой, лингводискурсивный, фолксономический анализ. В результате проведенного исследования были сделаны выводы о том, какими лингводискурсивными особенностями характеризуются посты топовых журналистов в популярных социальных сетях; как характеризуются лингвистические паттерны, продуцируемые топовыми журналистами в онлайн-пространстве; как различается контент, создаваемый журналистами в разных социальных сетях; каковы особенности этих различий в зависимости от специфики самих социальных платформ; как влияет политический контекст на лингвистические паттерны, продуцируемые топовыми журналистами в онлайн-пространстве. The article presents the results of a comprehensive analysis of the linguistic communication patterns of top Russian journalists in the official accounts of the social platforms VKontakte, Facebook, Instagram, Twitter. The purpose of this article is to study the linguistic patterns which are produced by the top journalists in their online accounts and which can set vectors of interpretation of political content created by state leaders and cause the transformation of discourse fields both in online and offline spaces. The average Russian spends almost half a day on online interaction, almost 50% of this time is spent on popular social media, including surfing the top journalists’ official accounts. The linguistic patterns produced by journalists in their online accounts are capable of transforming discursive fields both online and offline. The consumption of these patterns by ordinary users / readers who are under the influence of the discourse field becomes a determining factor in the process of making individual / collective decisions, the implementation of which forms a particular social action both in online and offline spaces. According to “Mediologia” monitoring data of social and mass media, the authors selected the accounts of Aleksey Venediktov, Vladimir Solovyev, Vladimir Pozner, Margarita Simonyan, and Ksenia Sobchak in VKontakte, Facebook, Instagram, Twitter. The data sets of the study are all the posts, comments, and threads of discussions that reflect the reaction of the above-mentioned journalists and the public to the Presidential Address to the Federal Assembly on 15 January 2020. The data sets were gained through a continuous sampling method and underwent a comprehensive analysis including network, linguo-discursive, folksonomic analyses. As a result of the study, the authors have drawn the conclusions on what linguistic and discursive features characterize the posts of the top journalists in popular social networks; the way the linguistic patterns produced by the top journalists in online space are characterized; the way the content created by the journalists in various social networks differs; what is the specificity of these differences depending on the specificity of the social platforms themselves; the way a political context affects the linguistic patterns produced by the top journalists in online space.

scholarly journals Inferring Ties in Social IoT Using Location-Based Networks and Identification of Hidden Suspicious Ties

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Nauman Ali Khan ◽  
Sihai Zhang ◽  
Wuyang Zhou ◽  
Ahmad Almogren ◽  
Ikram Ud Din ◽  
...  
Keyword(s):  
Social Networks ◽  
Internet Of Things ◽  
Social Ties ◽  
Social Activity ◽  
Geographical Location ◽  
Real Data ◽  
Time Frame ◽  
Data Sets ◽  
The Social ◽  
Time Frames

Stochastic Internet of Things (IoT)-based communication behavior of the progressing world is tremendously impacting social networks. The growth of social networks helps to quantify the effect on the Social Internet of Things (SIoT). Multiple existences of two persons at several geographical locations in different time frames hint to predict the social connection. We investigate the extent to which social ties between people can be inferred by critically reviewing the social networks. Our study used Chinese telecommunication-based anonymized caller data records (CDRs) and two openly available location-based social network data sets, Brightkite and Gowalla. Our research identified social ties based on mobile communication data and further exploits communication reasons based on geographical location. This paper presents an inference framework that predicts the missing ties as suspicious social connections using pipe and filter architecture-based inference framework. It highlights the secret relationship of users, which does not exist in real data. The proposed framework consists of two major parts. Firstly, users’ cooccurrence based on the mutual location in a specific time frame is computed and inferred as social ties. Results are investigated based upon the cooccurrence count, the gap time threshold values, and mutual friend count values. Secondly, the detail about direct connections is collected and cross-related to the inferred results using Precision and Recall evaluation measures. In the later part of the research, we examine the false-positive results methodically by studying the human cooccurrence patterns to identify hidden relationships using a social activity. The outcomes indicate that the proposed approach achieves comprehensive results that further support the theory of suspicious ties.

scholarly journals Temporal Label Walk for Community Detection and Tracking in Temporal Network

2019 ◽  
Vol 9 (15) ◽  
pp. 3199 ◽  
Author(s):  
Zheliang Liu ◽  
Hongxia Wang ◽  
Lizhi Cheng ◽  
Wei Peng ◽  
Xiang Li
Keyword(s):  
Community Detection ◽  
Time Window ◽  
Window Size ◽  
Real Data ◽  
Initial Structure ◽  
Data Sets ◽  
Temporal Network ◽  
Incremental Analysis ◽  
Detection And Tracking

The problem of temporal community detection is discussed in this paper. Main existing methods are either structure-based or incremental analysis. The difficulty of the former is to select a suitable time window. The latter needs to know the initial structure of networks and the changing of networks should be stable. For most real data sets, these conditions hardly prevail. A streaming method called Temporal Label Walk (TLW) is proposed in this paper, where the aforementioned restrictions are eliminated. Modularity of the snapshots is used to evaluate our method. Experiments reveal the effectiveness of TLW on temporal community detection. Compared with other static methods in real data sets, our method keeps a higher modularity with the increase of window size.

COMMUNITY DETECTION IN NETWORKS

2010 ◽  
Vol 20 (02) ◽  
pp. 361-367 ◽  
Author(s):  
C. O. DORSO ◽  
A. D. MEDUS
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
New Method

The problem of community detection is relevant in many disciplines of science. A community is usually defined, in a qualitative way, as a subset of nodes of a network which are more connected among themselves than to the rest of the network. In this article, we introduce a new method for community detection in complex networks. We define new merit factors based on the weak and strong community definitions formulated by Radicchi et al. [2004] and we show that this local definition properly describes the communities observed experimentally in two typical social networks.

Penguins Search Optimization Algorithm for Community Detection in Complex Networks

2018 ◽  
Vol 9 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Mohamed Guendouz ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Search Optimization ◽  
Density Evaluation ◽  
Evaluation Measure ◽  
Modularity Density ◽  
Initialization Strategy

In the last decade, the problem of community detection in complex networks has attracted the attention of many researchers in many domains, several methods and algorithms have been proposed to deal with this problem, many of them consider it as an optimization problem and various bio-inspired algorithms have been applied to solve it. In this work, the authors propose a new method for community detection in complex networks using the Penguins Search Optimization Algorithm (PeSOA), the authors use the modularity density evaluation measure as a function to maximize and they propose also to enhance the algorithm by using a new initialization strategy. The proposed algorithm has been tested on four popular real-world networks; experimental results compared with other known algorithms show the effectiveness of using this method for community detection in social networks.

scholarly journals Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 15-32
Author(s):  
Ehsan Ardjmand ◽  
William A. Young II ◽  
Najat E. Almasarwah
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Academic Community ◽  
Detection Methods ◽  
The Unconscious ◽  
Comprehensive Review ◽  
Wide Range

Detecting the communities that exist within complex social networks has a wide range of application in business, engineering, and sociopolitical settings. As a result, many community detection methods are being developed by researchers in the academic community. If the communities within social networks can be more accurately detected, the behavior or characteristics of each community within the networks can be better understood, which implies that better decisions can be made. In this paper, a discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods. The comparative study shows that the unconscious search algorithm consistently produced the highest modularity that was discovered through the comprehensive review of the literature.

scholarly journals Modularity based Community Detection in Social Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 564-569
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Research Community ◽  
Incorrect Result ◽  
The Real

The community detection is an interesting and highly focused area in the analysis of complex networks (CNA). It identifies closely connected clusters of nodes. In recent years, several approaches have been proposed for community detection and validation of the result. Community detection approaches that use modularity as a measure have given much weight-age by the research community. Various modularity based community detection approaches are given for different domains. The network in the real-world may be weighted, heterogeneous or dynamic. So, it is inappropriate to apply the same algorithm for all types of networks because it may generate incorrect result. Here, literature in the area of community detection and the result evaluation has been extended with an aim to identify various shortcomings. We think that the contribution of facts given in this paper can be very useful for further research.

scholarly journals An efficient method to detect communities in social networks

2021 ◽  
Author(s):  
MEHJABIN KHATOON ◽  
W AISHA BANU
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Community Detection ◽  
Average Degree ◽  
Data Sets ◽  
Time Data ◽  
Network Graph ◽  
The Social ◽  
Metric Functions

Abstract Social networks represent the social structure, which is composed of individuals having social interactions among them. The interactions between the units in a social network represent the relations of the various social contacts and aim at finding different individuals in that network, with similar interests. It is a challenging problem to detect the social interactions between individuals with comparable considerations and desires from a large social network, which can be termed as community detection. Detection of the communities from social networks has been done by other authors previously, and many community identification algorithms were also proposed, but those communities' identification has been achieved on the online available data sets. The proposed algorithm in this paper has been named as Average Degree Newman Girvan (ADNG) algorithm, which can easily identify the communities from the real-time data sets, collected from the social network websites. The approach presented here is based on first determining the average degree of the network graph and then identifying the communities using the Newman Girvan algorithm. The proposed algorithm has been compared with four community detection algorithms, i.e., Leading eigenvector (LEC) algorithm, Fastgreedy (FG) algorithm, Leiden algorithm and Kernighan-Lin (KL) algorithm based on a few metric functions. This algorithm helps to detect communities for different domains, like for any proposed government policy, online shopping products, newly launched products in a market, etc.

Social-Spatial Group Queries with Keywords

2022 ◽  
Vol 8 (1) ◽  
pp. 1-32
Author(s):  
Sajid Hasan Apon ◽  
Mohammed Eunus Ali ◽  
Bishwamittra Ghosh ◽  
Timos Sellis
Keyword(s):  
Social Networks ◽  
Social Groups ◽  
Real Life ◽  
Real Data ◽  
Data Sets ◽  
Interesting Insight ◽  
Points Of Interest ◽  
The Social ◽  
Textual Data ◽  
Aware Group

Social networks with location enabling technologies, also known as geo-social networks, allow users to share their location-specific activities and preferences through check-ins. A user in such a geo-social network can be attributed to an associated location (spatial), her preferences as keywords (textual), and the connectivity (social) with her friends. The fusion of social, spatial, and textual data of a large number of users in these networks provide an interesting insight for finding meaningful geo-social groups of users supporting many real-life applications, including activity planning and recommendation systems. In this article, we introduce a novel query, namely, Top- k Flexible Socio-Spatial Keyword-aware Group Query (SSKGQ), which finds the best k groups of varying sizes around different points of interest (POIs), where the groups are ranked based on the social and textual cohesiveness among members and spatial closeness with the corresponding POI and the number of members in the group. We develop an efficient approach to solve the SSKGQ problem based on our theoretical upper bounds on distance, social connectivity, and textual similarity. We prove that the SSKGQ problem is NP-Hard and provide an approximate solution based on our derived relaxed bounds, which run much faster than the exact approach by sacrificing the group quality slightly. Our extensive experiments on real data sets show the effectiveness of our approaches in different real-life settings.

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