scholarly journals Business Relationship Network Model from Social Reactions Data

2020 ◽  
Author(s):  
Diego Paolo Tsutsumi ◽  
Thiago Henrique Silva
Keyword(s):  
Social Media ◽  
Network Model ◽  
Real Data ◽  
Detection Algorithm ◽  
Business Community ◽  
Business Partnerships ◽  
Business Relationship ◽  
Business Partnership ◽  
Relationship Network ◽  
Community Detection Algorithm

One of the primary ways to expand a business or to keep it stable during a crisis is to create partnerships with other companies. With that, this study presents results regarding a new data model, which explores user reactions on social media to indicate strategic business partnerships. Th ere are three main contributions of this study to the literature: (i) a business relationship network model; (ii) a business community detection algorithm; and (iii) a business outlier detection algorithm. The evaluation of the contributions was performed exploring real data of approximately 280 million user reactions on Facebook. Results suggest that business partnership recommendation is possible using the information available in social media.

Parallel Network Analysis and Communities Detection (PANC) Pipeline for the Analysis and Visualization of COVID-19 Data

2021 ◽  
pp. 2142002
Author(s):  
Giuseppe Agapito ◽  
Marianna Milano ◽  
Mario Cannataro
Keyword(s):  
Network Analysis ◽  
Real Data ◽  
Detection Algorithm ◽  
Data Sets ◽  
Data Set ◽  
Italian Regions ◽  
Initial Dataset ◽  
Parallel Network ◽  
Community Detection Algorithm ◽  
Similarity Matrices

A new coronavirus, causing a severe acute respiratory syndrome (COVID-19), was started at Wuhan, China, in December 2019. The epidemic has rapidly spread across the world becoming a pandemic that, as of today, has affected more than 70 million people causing over 2 million deaths. To better understand the evolution of spread of the COVID-19 pandemic, we developed PANC (Parallel Network Analysis and Communities Detection), a new parallel preprocessing methodology for network-based analysis and communities detection on Italian COVID-19 data. The goal of the methodology is to analyze set of homogeneous datasets (i.e. COVID-19 data in several regions) using a statistical test to find similar/dissimilar behaviours, mapping such similarity information on a graph and then using community detection algorithm to visualize and analyze the initial dataset. The methodology includes the following steps: (i) a parallel methodology to build similarity matrices that represent similar or dissimilar regions with respect to data; (ii) an effective workload balancing function to improve performance; (iii) the mapping of similarity matrices into networks where nodes represent Italian regions, and edges represent similarity relationships; (iv) the discovering and visualization of communities of regions that show similar behaviour. The methodology is general and can be applied to world-wide data about COVID-19, as well as to all types of data sets in tabular and matrix format. To estimate the scalability with increasing workloads, we analyzed three synthetic COVID-19 datasets with the size of 90.0[Formula: see text]MB, 180.0[Formula: see text]MB, and 360.0[Formula: see text]MB. Experiments was performed on showing the amount of data that can be analyzed in a given amount of time increases almost linearly with the number of computing resources available. Instead, to perform communities detection, we employed the real data set.

scholarly journals Is it correct to project and detect? How weighting unipartite projections influences community detection

2020 ◽  
Vol 8 (S1) ◽  
pp. S145-S163 ◽  
Author(s):  
Tristan J. B. Cann ◽  
Iain S. Weaver ◽  
Hywel T. P. Williams
Keyword(s):  
Social Media ◽  
Community Structure ◽  
Community Detection ◽  
Detection Algorithm ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Algorithm Performance ◽  
Projection Scheme ◽  
Community Detection Algorithm ◽  
Experimental Findings

AbstractBipartite networks represent pairwise relationships between nodes belonging to two distinct classes. While established methods exist for analyzing unipartite networks, those for bipartite network analysis are somewhat obscure and relatively less developed. Community detection in such instances is frequently approached by first projecting the network onto a unipartite network, a method where edges between node classes are encoded as edges within one class. Here we test seven different projection schemes by assessing the performance of community detection on both: (i) a real-world dataset from social media and (ii) an ensemble of artificial networks with prescribed community structure. A number of performance and accuracy issues become apparent from the experimental findings, especially in the case of long-tailed degree distributions. Of the methods tested, the “hyperbolic” projection scheme alleviates most of these difficulties and is thus the most robust scheme of those tested. We conclude that any interpretation of community detection algorithm performance on projected networks must be done with care as certain network configurations require strong community preference for the bipartite structure to be reflected in the unipartite communities. Our results have implications for the analysis of detected community structure in projected unipartite networks.

Conflict Detection Techniques for Preserving Privacy in Social Media

2018 ◽  
pp. 78-85
Author(s):  
Fulpagare Priya K. ◽  
Nitin N. Patil
Keyword(s):  
Social Media ◽  
Privacy Management ◽  
Crucial Importance ◽  
Network Integration ◽  
Critical Problem ◽  
Detection Techniques ◽  
Privacy Preferences ◽  
Content Sharing ◽  
Privacy Settings ◽  
Relationship Network

Social Network is an emerging e-service for Content Sharing Sites (CSS). It is an emerging service which provides reliable communication. Some users over CSS affect user’s privacy on their personal contents, where some users keep on sending annoying comments and messages by taking advantage of the user’s inherent trust in their relationship network. Integration of multiple user’s privacy preferences is very difficult task, because privacy preferences may create conflict. The techniques to resolve conflicts are essentially required. Moreover, these methods need to consider how users would actually reach an agreement about a solution to the conflict in order to offer solutions acceptable by all of the concerned users. The first mechanism to resolve conflicts for multi-party privacy management in social media that is able to adapt to different situations by displaying the enterprises that users make to reach a result to the conflicts. Billions of items that are uploaded to social media are co-owned by multiple users. Only the user that uploads the item is allowed to set its privacy settings (i.e. who can access the item). This is a critical problem as users’ privacy preferences for co-owned items can conflict. Multi-party privacy management is therefore of crucial importance for users to appropriately reserve their privacy in social media.

Pengembangan Kemitraan Koperasi, Usaha Mikro Dan Kecil (KUMK) Dengan Usaha Menengah/Besar Untuk Komoditi Unggulan Lokal

2019 ◽  
Vol 9 (1) ◽  
pp. 53-66 ◽  
Author(s):  
Dandan Irawan
Keyword(s):  
Business Performance ◽  
The Other ◽  
Economic Conditions ◽  
Small Enterprises ◽  
Business Partnerships ◽  
Short Term ◽  
Business Partnership ◽  
Structure Factors ◽  
Micro And Small Enterprises ◽  
Business Entities

Basically a natural partnership will achieve its goal if mutual requirements, mutual reinforcement, and mutual benefit can be maintained and made a strong fundamental commitment among partners. Nevertheless the development seems very slow. The cause is the presence of specific and different conditions and structure factors compared to other countries. Along with that, we still encounter various forms of gaps, such as inequality among regions, among income groups, between sectors, among economic actors, and so forth. The next problem is that in business entities including cooperatives and micro and small enterprises in running their business activities requires business partnerships with medium and large enterprises in order to improve business performance and business scale. While on the other hand our economic conditions and structures are not yet fully conducive to fostering partnerships based on purely business considerations or competitive market motivations but the business partnership of the foundation is strong enough in our country's constitution. Partnerships will work if partners are equally benefiting. Our concept of partnership is like that, although in the short term, there is a party or a party benefiting more from the other side.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

scholarly journals Risk Management in Seaports: A Community Analysis at the Port of Hamburg

2021 ◽  
Vol 13 (14) ◽  
pp. 8035
Author(s):  
Ayman Nagi ◽  
Meike Schroeder ◽  
Wolfgang Kersten
Keyword(s):  
Risk Management ◽  
Natural Disasters ◽  
Secondary Data ◽  
Community Analysis ◽  
Detection Algorithm ◽  
Structured Interviews ◽  
Emergency Plans ◽  
Plausibility Check ◽  
Safety Risks ◽  
Community Detection Algorithm

The aim of this work is to detect communities of stakeholders at the port of Hamburg regarding their communication intensity in activities related to risk management. An exploratory mixed-method design is chosen as a methodology based on a compact survey and semi-structured interviews, as well as secondary data. A compact survey at the port of Hamburg is utilized to address the communication intensity values among stakeholders. Based on 28 full responses, the data is extracted, cleansed, and prepared for the network analysis using the software “Gephi”. Thereafter, the Louvain community detection algorithm is used to extract the communities from the network. A plausibility check is carried out using 15 semi-structured interviews and secondary data to verify and refine the results of the community analysis. The results have revealed different communities for the following risk categories: (a) natural disasters and (b) operational and safety risks. The focus of cooperation is on the reactive process and emergency plans. For instance, emergency plans play an important role in the handling of natural disasters such as floods or extreme winds.

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