scholarly journals A New Algorithm to Detect and Evaluate Learning Communities in Social Networks: Facebook Groups

2019 ◽  
Vol 14 (23) ◽  
pp. 165
Author(s):  
Meriem Adraoui ◽  
Asmaâ Retbi ◽  
Mohammed Khalidi Idrissi ◽  
Samir Bennani
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Learning Communities ◽  
Community Detection ◽  
New Method ◽  
Second Phase ◽  
Community Learning ◽  
Detection Algorithms ◽  
Two Phases ◽  
Set Up

This article aims to present a new method of evaluating learners by communities on Facebook groups which based on their interactions. The objective of our study is to set up a community learning structure according to the learners' levels. In this context, we have proposed a new algorithm to detect and evaluate learning communities. Our algorithm consists of two phases. The first phase aims to evaluate learners by measuring their degrees of ‘Safely’. The second phase is used to detect communities. These two phases will be repeated until the best community structure is found. Finally, we test the performance of our proposed approach on five Facebook groups. Our algorithm gives good results compared to other community detection algorithms.

scholarly journals Neighbor Similarity Based Agglomerative Method for Community Detection in Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Jianjun Cheng ◽  
Xing Su ◽  
Haijuan Yang ◽  
Longjie Li ◽  
Jingming Zhang ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Second Phase ◽  
Community Structures ◽  
Node Similarity ◽  
Local Methods ◽  
Two Phases ◽  
Comparison Algorithms ◽  
New Community ◽  
The One

Community structures can reveal organizations and functional properties of complex networks; hence, detecting communities from networks is of great importance. With the surge of large networks in recent years, the efficiency of community detection is demanded critically. Therefore, many local methods have emerged. In this paper, we propose a node similarity based community detection method, which is also a local one consisted of two phases. In the first phase, we first take out the node with the largest degree from the network to take it as an exemplar of the first community and insert its most similar neighbor node into the community as well. Then, the one with the largest degree in the remainder nodes is selected; if its most similar neighbor has not been classified into any community yet, we create a new community for the selected node and its most similar neighbor. Otherwise, if its most similar neighbor has been classified into a certain community, we insert the selected node into the community to which its most similar neighbor belongs. This procedure is repeated until every node in the network is assigned to a community; at that time, we obtain a series of preliminary communities. However, some of them might be too small or too sparse; edges connecting to outside of them might go beyond the ones inside them. Keeping them as the final ones will lead to a low-quality community structure. Therefore, we merge some of them in an efficient approach in the second phase to improve the quality of the resulting community structure. To testify the performance of our proposed method, extensive experiments are performed on both some artificial networks and some real-world networks. The results show that the proposed method can detect high-quality community structures from networks steadily and efficiently and outperform the comparison algorithms significantly.

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.

A Survey of Parallel Community Detection Algorithms

2017 ◽  
pp. 1-26
Author(s):  
Sobin C. C. ◽  
Vaskar Raychoudhury ◽  
Snehanshu Saha
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Community Structure ◽  
Community Detection ◽  
Social Relations ◽  
Online Social Networks ◽  
Detection Algorithms ◽  
Big Data Applications ◽  
Comprehensive Survey ◽  
External Connections

The amount of data generated by online social networks such as Facebook, Twitter, etc., has recently experienced an enormous growth. Extracting useful information such as community structure, from such large networks is very important in many applications. Community is a collection of nodes, having dense internal connections and sparse external connections. Community detection algorithms aim to group nodes into different communities by extracting similarities and social relations between nodes. Although, many community detection algorithms in literature, they are not scalable enough to handle large volumes of data generated by many of the today's big data applications. So, researchers are focusing on developing parallel community detection algorithms, which can handle networks consisting of millions of edges and vertices. In this article, we present a comprehensive survey of parallel community detection algorithms, which is the first ever survey in this domain, although, multiple papers exist in literature related to sequential community detection algorithms.

Spectral Synchronization Reveals Community Structure

2015 ◽  
Vol 719-720 ◽  
pp. 1198-1202
Author(s):  
Ming Yang Zhou ◽  
Zhong Qian Fu ◽  
Zhao Zhuo
Keyword(s):  
Spectral Analysis ◽  
Community Structure ◽  
Community Detection ◽  
Hierarchical Structure ◽  
Edge Density ◽  
Complex Structures ◽  
Detection Algorithms ◽  
Dimension Space ◽  
Local Synchronization ◽  
Better Than

Practical networks have community and hierarchical structure. These complex structures confuse the community detection algorithms and obscure the boundaries of communities. This paper proposes a delicate method which synthesizes spectral analysis and local synchronization to detect communities. Communities emerge automatically in the multi-dimension space of nontrivial eigenvectors. Its performance is compared to that of previous methods and applied to different practical networks. Our results perform better than that of other methods. Besides, it’s more robust for networks whose communities have different edge density and follow various degree distributions. This makes the algorithm a valuable tool to detect and analysis large practical networks with various community structures.

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 A Seed-Expanding Method Based on TOPSIS for Community Detection in Complex Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jianjun Cheng ◽  
Wenbo Zhang ◽  
Haijuan Yang ◽  
Xing Su ◽  
Tao Ma ◽  
...  
Keyword(s):  
Decision Making ◽  
Community Structure ◽  
Community Detection ◽  
Centrality Measures ◽  
Ideal Solution ◽  
Centrality Measure ◽  
Detection Algorithms ◽  
Pros And Cons ◽  
Topsis Technique

The centrality plays an important role in many community-detection algorithms, which depend on various kinds of centralities to identify seed vertices of communities first and then expand each of communities based on the seeds to get the resulting community structure. The traditional algorithms always use a single centrality measure to recognize seed vertices from the network, but each centrality measure has both pros and cons when being used in this circumstance; hence seed vertices identified using a single centrality measure might not be the best ones. In this paper, we propose a framework which integrates advantages of various centrality measures to identify the seed vertices from the network based on the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) multiattribute decision-making technology. We take each of the centrality measures involved as an attribute, rank vertices according to the scores which are calculated for them using TOPSIS, and then take vertices with top ranks as the seeds. To put this framework into practice, we concretize it in this paper by considering four centrality measures as attributes to identify the seed vertices of communities first, then expanding communities by iteratively inserting one unclassified vertex into the community to which its most similar neighbor belongs, and the similarity between them is the largest among all pairs of vertices. After that, we obtain the initial community structure. However, the amount of communities might be much more than they should be, and some communities might be too small to make sense. Therefore, we finally consider a postprocessing procedure to merge some initial communities into larger ones to acquire the resulting community structure. To test the effectiveness of the proposed framework and method, we have performed extensive experiments on both some synthetic networks and some real-world networks; the experimental results show that the proposed method can get better results, and the quality of the detected community structure is much higher than those of competitors.

Pediatric blinatumomab preparation: Risk assessment on SmPC for software compliance

2020 ◽  
pp. 107815522096639
Author(s):  
Mario Cirino ◽  
Riccardo Provasi ◽  
Irina Cebulec ◽  
Clara Palmieri ◽  
Paolo Schincariol ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Poor Prognosis ◽  
Computer Processing ◽  
Second Phase ◽  
Preparation Process ◽  
Critical Issues ◽  
Adults And Children ◽  
Two Phases ◽  
Set Up ◽  
Cancer In Children

Introduction Blinatumomab is an anticancer drug used in the treatment of Acute Lymphoblastic Leukaemia (ALL) in both adults and children. ALL is the most common form of cancer in children and patients who are refractory to standard treatments have poor prognosis. The preparation of blinatumomab is unique and extremely complex. It’s important to carry out any information to identify all the critical issues related to the preparation of blinatumomab: sharing procedure between prescribers, staff of the Centralized Chemotherapy Preparation Unit [Unità Farmaci Antiblastici (UFA)] and administering nurses aimed at reducing the clinical risk related to the management of the drug blinatumomab and to obtain correct prescriptions on the real dose to be prepared, safe worksheets with computer processing of all variables (volumes to be added and corresponding dose of drug) and complete labels containing all the information necessary for the control of the preparation and its correct infusion. Methods A computerized process involves the use of specific software to which precise instructions must be given. This study is divided into two phases, the first one focused on the analysis of Summary of Product Characteristics (SmPC) and the extrapolation of any unclear part of SmPC. The second phase involved the manufacturer to answer a questionnaire. Results This comparison with the company allowed to perfect the blinatumomab preparation process leading to: 1. allow the patient to be discharged and return a few times for infusions and consequently reduce the number of medical prescriptions; 2. set up the drug for each patient every 4 days; 3. reduce costs related to devices, staff employed. Conclusion Computerizing the preparation of anti-blastic drugs is a necessary path for the safety of the patient and all the operators involved, however it may be necessary to make changes in the preparation process to allow the software to work correctly. The comparison between pharmacist, clinician and, where necessary, the manufacturer of the drug, was effective in the preparation of this drug.

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.

Node Trust: an effective method to detect non-overlapping community in social networks

2020 ◽  
pp. 2150036
Author(s):  
Jinfang Sheng ◽  
Qiong Li ◽  
Bin Wang ◽  
Wanghao Guan ◽  
Jinying Dai ◽  
...  
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
Community Detection ◽  
Linear Time ◽  
Deep Understanding ◽  
Detection Algorithm ◽  
Underlying Structure ◽  
Community Detection Algorithm ◽  
Stable Community ◽  
Synthetic Networks

Social networks are made up of members in society and the social relationships established by the interaction between members. Community structure is an essential attribute of social networks. The question arises that how can we discover the community structure in the network to gain a deep understanding of its underlying structure and mine information from it? In this paper, we introduce a novel community detection algorithm NTCD (Community Detection based on Node Trust). This is a stable community detection algorithm that does not require any parameters settings and has nearly linear time complexity. NTCD determines the community ownership of a node by studying the relationship between the node and its neighbor communities. This relationship is called Node Trust, representing the possibility that the node is in the current community. Node Trust is also a quality function, which is used for community detection by seeking maximum. Experiments on real and synthetic networks show that our algorithm has high accuracy in most data sets and stable community division results. Additionally, through experiments on different types of synthetic networks, we can conclude that our algorithm has good robustness.

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