scholarly journals A systematic comparison of community detection algorithms for measuring selective exposure in co-exposure networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Subhayan Mukerjee
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Selective Exposure ◽  
Model Parameters ◽  
Data Set ◽  
Media Environment ◽  
Systematic Comparison ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Audience Fragmentation

AbstractThe use of community detection techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. However, there exists no systematic comparison, that seeks to identify which of the many community detection algorithms are the best suited for studying these dynamics. In this paper, I address this question by proposing a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific parameters, that control various aspects of the media environment and individual behavior. I then use a variety of community detection algorithms to characterize the level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. I demonstrate how changing the manner in which co-exposure networks are constructed significantly improves the performances of some of these algorithms. Finally, I validate these findings using a novel empirical data-set of large-scale browsing behavior. The contributions of this research are two-fold: first, it shows that two specific algorithms, FastGreedy and Multilevel are the best suited for measuring selective exposure patterns in co-exposure networks. Second, it demonstrates the use of formal modeling for informing analytical choices for better capturing complex social phenomena.

scholarly journals Measuring Selective Exposure: A Systematic Comparison of Community Detection Algorithms in Coexposure Networks

2020 ◽  
Author(s):  
Subhayan Mukerjee
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Selective Exposure ◽  
Model Parameters ◽  
Data Set ◽  
Media Environment ◽  
Detection Algorithms ◽  
Audience Fragmentation ◽  
The Media ◽  
Browsing Behavior

The use of network analytic techniques for understanding audience fragmentation and selective exposure to information has received substantial scholarly attention in recent years. In this paper, I propose a formal mathematical model for audience co-exposure networks by simulating audience behavior in an artificial media environment. I show how a variety of synthetic audience overlap networks can be generated by tuning specific parameters, that control various aspects of the media environment and individual behavior. I then use a variety of community detection algorithms to characterize the level of audience fragmentation in these synthetic networks and compare their performances for different combinations of the model parameters. Finally, I validate these findings using a novel empirical data-set of actual large-scale browsing behavior and demonstrate the model's utility in informing future analytical choices.

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.

scholarly journals Performance Evaluation of Modularity Based Community Detection Algorithms in Large Scale Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Vinícius da Fonseca Vieira ◽  
Carolina Ribeiro Xavier ◽  
Nelson Francisco Favilla Ebecken ◽  
Alexandre Gonçalves Evsukoff
Keyword(s):  
Spectral Method ◽  
Community Detection ◽  
High Performance ◽  
Large Scale ◽  
Fine Tuning ◽  
Detection Algorithms ◽  
Research Areas ◽  
Structure Detection ◽  
Wide Range ◽  
Large Scale Networks

Community structure detection is one of the major research areas of network science and it is particularly useful for large real networks applications. This work presents a deep study of the most discussed algorithms for community detection based on modularity measure: Newman’s spectral method using a fine-tuning stage and the method of Clauset, Newman, and Moore (CNM) with its variants. The computational complexity of the algorithms is analysed for the development of a high performance code to accelerate the execution of these algorithms without compromising the quality of the results, according to the modularity measure. The implemented code allows the generation of partitions with modularity values consistent with the literature and it overcomes 1 million nodes with Newman’s spectral method. The code was applied to a wide range of real networks and the performances of the algorithms are evaluated.

Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review

2020 ◽  
Vol 13 (4) ◽  
pp. 542-549
Author(s):  
Smita Agrawal ◽  
Atul Patel
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Connected Subgraph ◽  
Comprehensive Review ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Large Scale Networks

Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the densely connected subgraph from the complex network, various community detection algorithms using clustering techniques are discussed here. In this paper, we discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them. In the end, insights into the future scope and research opportunities for community detection are discussed.

New contributions for the comparison of community detection algorithms in attributed networks

2020 ◽  
Vol 8 (4) ◽  
Author(s):  
Ana Rita Vieira ◽  
Pedro Campos ◽  
Paula Brito
Keyword(s):  
Community Detection ◽  
Network Topology ◽  
Vector Data ◽  
Task Partitioning ◽  
Clustering Techniques ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
The Past ◽  
Networks Structure ◽  
Attributed Networks

Abstract Community detection techniques use only the information about the network topology to find communities in networks. Similarly, classic clustering techniques for vector data consider only the information about the values of the attributes describing the objects to find clusters. In real-world networks, however, in addition to the information about the network topology, usually there is information about the attributes describing the vertices that can also be used to find communities. Using both the information about the network topology and about the attributes describing the vertices can improve the algorithms’ results. Therefore, authors started investigating methods for community detection in attributed networks. In the past years, several methods were proposed to uncover this task, partitioning a graph into sub-graphs of vertices that are densely connected and similar in terms of their descriptions. This article focuses on the analysis and comparison of some of the proposed methods for community detection in attributed networks. For that purpose, several applications to both synthetic and real networks are conducted. Experiments are performed on both weighted and unweighted graphs. The objective is to establish which methods perform generally better according to the validation measures and to investigate their sensitivity to changes in the networks’ structure and homogeneity.

scholarly journals Multi Comm_Plus: A Community Detection System for Identification of Community in Multi-Dimensional Networks

2016 ◽  
Vol 4 (05) ◽  
pp. 9879-9884
Author(s):  
Dhanya Sudhakaran ◽  
Shini Renjith
Keyword(s):  
Big Data ◽  
Real Time ◽  
Community Detection ◽  
Data Analytics ◽  
Selection Criteria ◽  
Large Scale ◽  
Detection System ◽  
Big Data Analytics ◽  
Detection Algorithms ◽  
Large Scale Networks

Community detection is a common problem in graph and big data analytics. It consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. Community detection algorithms in literature proves to be less efficient, as it leads to generation of communities with noisy interactions. To address this limitation, there is a need to develop a system which identifies the best community among multi-dimensional networks based on relevant selection criteria and dimensionality of entities, thereby eliminating the noisy interactions in a real-time environment.

scholarly journals Detection and Analysis of Sow Targets Based on Image Vision

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Kaidong Lei ◽  
Chao Zong ◽  
Ting Yang ◽  
Shanshan Peng ◽  
Pengfei Zhu ◽  
...  
Keyword(s):  
Large Scale ◽  
Recognition Rate ◽  
Model Parameters ◽  
Livestock Farming ◽  
Data Set ◽  
Attention Model ◽  
Precision Livestock Farming ◽  
Area Distribution ◽  
Complex Scenes ◽  
Detection And Recognition

In large-scale sow production, real-time detection and recognition of sows is a key step towards the application of precision livestock farming techniques. In the pig house, the overlap of railings, floors, and sows usually challenge the accuracy of sow target detection. In this paper, a non-contact machine vision method was used for sow targets perception in complex scenarios, and the number position of sows in the pen could be detected. Two multi-target sow detection and recognition models based on the deep learning algorithms of Mask-RCNN and UNet-Attention were developed, and the model parameters were tuned. A field experiment was carried out. The data-set obtained from the experiment was used for algorithm training and validation. It was found that the Mask-RCNN model showed a higher recognition rate than that of the UNet-Attention model, with a final recognition rate of 96.8% and complete object detection outlines. In the process of image segmentation, the area distribution of sows in the pens was analyzed. The position of the sow’s head in the pen and the pixel area value of the sow segmentation were analyzed. The feeding, drinking, and lying behaviors of the sow have been identified on the basis of image recognition. The results showed that the average daily lying time, standing time, feeding and drinking time of sows were 12.67 h(MSE 1.08), 11.33 h(MSE 1.08), 3.25 h(MSE 0.27) and 0.391 h(MSE 0.10), respectively. The proposed method in this paper could solve the problem of target perception of sows in complex scenes and would be a powerful tool for the recognition of sows.

scholarly journals Efficient Estimation of Network Games of Incomplete Information: Application to Large Online Social Networks

2021 ◽  
Author(s):  
Xi Chen ◽  
Ralf van der Lans ◽  
Michael Trusov
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Incomplete Information ◽  
Community Detection ◽  
Online Social Networks ◽  
Choice Model ◽  
Real Life ◽  
Efficient Estimation ◽  
Data Set ◽  
Detection Algorithms

This paper presents a structural discrete choice model with social influence for large-scale social networks. The model is based on an incomplete information game and permits individual-specific parameters of consumers. It is challenging to apply this type of models to real-life scenarios for two reasons: (1) The computation of the Bayesian–Nash equilibrium is highly demanding; and (2) the identification of social influence requires the use of excluded variables that are oftentimes unavailable. To address these challenges, we derive the unique equilibrium conditions of the game, which allow us to employ a stochastic Bayesian estimation procedure that is scalable to large social networks. To facilitate the identification, we utilize community-detection algorithms to divide the network into different groups that, in turn, can be used to construct excluded variables. We validate the proposed structural model with the login decisions of more than 25,000 users of an online social game. Importantly, this data set also contains promotions that were exogenously determined and targeted to only a subgroup of consumers. This information allows us to perform exogeneity tests to validate our identification strategy using community-detection algorithms. Finally, we demonstrate the managerial usefulness of the proposed methodology for improving the strategies of targeting influential consumers in large social networks. This paper was accepted by Matthew Shum, marketing.

scholarly journals APPROACHES TO DISTINGUISH ‘REAL’ CHANGES FROM ‘UNREAL’ CHANGES BASED ON MULTI-TEMPORAL 2D BUILDING FOOTPRINT DATA

2018 ◽  
Vol XLII-4 ◽  
pp. 559-563 ◽  
Author(s):  
M. Schorcht ◽  
R. Hecht ◽  
G. Meinel
Keyword(s):  
Change Detection ◽  
Large Scale ◽  
Geospatial Data ◽  
Data Sets ◽  
Building Stock ◽  
Data Set ◽  
Detection Techniques ◽  
European Settlement ◽  
Multi Temporal ◽  
Building Footprint

<p><strong>Abstract.</strong> Building footprint data from National Mapping and Cadastral Agencies are available in Germany for 7 years as a uniform, nation-wide geospatial data set and are updated annually. These multi-temporal building data sets can form the basis for the application of change detection techniques to derive national figures on dynamics in the building stock. Since these building data sets have only been built up in recent years, it is necessary to distinguish real changes from false changes. This is done by applying vector geometry-based operations and statistical analyses, which are presented in this article. Furthermore, by the additional use of the raster dataset Copernicus &amp;ndash; European Settlement Map (classified, resolution 2.5<span class="thinspace"></span>m) it is approximately possible to estimate whether it is a correct change or not. The advantage of this approach is that large-scale comparable results can be derived simply and quickly based on uniform basic data.</p>

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