scholarly journals Community Detection in Airline Networks: An Empirical Analysis of American vs. Southwest Airlines

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Weiwei Wu ◽  
Haoyu Zhang ◽  
Shengrun Zhang ◽  
Frank Witlox
Keyword(s):  
Case Studies ◽  
Empirical Analysis ◽  
Community Detection ◽  
Topological Structure ◽  
Community Structures ◽  
Operation Pattern ◽  
Airline Networks ◽  
Modularity Maximization ◽  
Influential Nodes ◽  
Southwest Airlines

In this paper, we develop a route-traffic-based method for detecting community structures in airline networks. Our model is both an application and an extension of the Clauset-Newman-Moore (CNM) modularity maximization algorithm, in that we apply the CNM algorithm to large airline networks, and take both route distance and passenger volumes into account. Therefore, the relationships between airports are defined not only based on the topological structure of the network but also by a traffic-driven indicator. To illustrate our model, two case studies are presented: American Airlines and Southwest Airlines. Results show that the model is effective in exploring the characteristics of the network connections, including the detection of the most influential nodes and communities on the formation of different network structures. This information is important from an airline operation pattern perspective to identify the vulnerability of networks.

scholarly journals Reduction of the molecular hamiltonian matrix using quantum community detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Susan M. Mniszewski ◽  
Pavel A. Dub ◽  
Sergei Tretiak ◽  
Petr M. Anisimov ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Community Detection ◽  
Slater Determinant ◽  
Hamiltonian Matrix ◽  
Approximate Methods ◽  
Molecular Systems ◽  
Hartree Fock ◽  
Molecular Hamiltonian ◽  
Structure Problem ◽  
Modularity Maximization ◽  
Chemical Knowledge

AbstractQuantum chemistry is interested in calculating ground and excited states of molecular systems by solving the electronic Schrödinger equation. The exact numerical solution of this equation, frequently represented as an eigenvalue problem, remains unfeasible for most molecules and requires approximate methods. In this paper we introduce the use of Quantum Community Detection performed using the D-Wave quantum annealer to reduce the molecular Hamiltonian matrix in Slater determinant basis without chemical knowledge. Given a molecule represented by a matrix of Slater determinants, the connectivity between Slater determinants (as off-diagonal elements) is viewed as a graph adjacency matrix for determining multiple communities based on modularity maximization. A gauge metric based on perturbation theory is used to determine the lowest energy cluster. This cluster or sub-matrix of Slater determinants is used to calculate approximate ground state and excited state energies within chemical accuracy. The details of this method are described along with demonstrating its performance across multiple molecules of interest and bond dissociation cases. These examples provide proof-of-principle results for approximate solution of the electronic structure problem using quantum computing. This approach is general and shows potential to reduce the computational complexity of post-Hartree–Fock methods as future advances in quantum hardware become available.

scholarly journals Community Detection in Multiplex Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Matteo Magnani ◽  
Obaida Hanteer ◽  
Roberto Interdonato ◽  
Luca Rossi ◽  
Andrea Tagarelli
Keyword(s):  
Community Detection ◽  
Experimental Evaluation ◽  
Network Models ◽  
Ground Truth ◽  
Community Structures ◽  
Multiplex Networks ◽  
Detection Algorithms ◽  
Multiplex Network ◽  
The Right ◽  
Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

Incremental Community Detection on Large Complex Attributed Network

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Zhe Chen ◽  
Aixin Sun ◽  
Xiaokui Xiao
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Network Data ◽  
Topological Information ◽  
Community Membership ◽  
Attributed Network ◽  
Benchmark Datasets ◽  
Modularity Maximization ◽  
Large Scale Networks

Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains users with transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.

Community Detection in Political Discussions on Twitter

2016 ◽  
Vol 35 (2) ◽  
pp. 244-261 ◽  
Author(s):  
Frederic Guerrero-Solé
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Reliable Method ◽  
Community Structures ◽  
Network Community ◽  
High Polarization

In November 9, 2014, the Catalan government called Catalan people to participate in a straw poll about the independence of Catalonia from Spain. This article analyzes the use of Twitter between November 8 and 10, 2014. Drawing on a methodology developed by Guerrero-Solé, Corominas-Murtra, and Lopez-Gonzalez, this work examines the structure of the retweet overlap network (RON), formed by those users whose communities of retweeters have nonzero overlapping, to detect the community structure of the network. The results show a high polarization of the resulting network and prove that the RON is a reliable method to determinate network community structures and users’ political leaning in political discussions.

‘Islam Does Not Belong to Them’: Ethnic and Religious Identities Among Male Igbo Converts in Hausaland

Africa ◽  
2000 ◽  
Vol 70 (3) ◽  
pp. 422-441 ◽  
Author(s):  
Douglas Anthony
Keyword(s):  
Social Networks ◽  
Civil War ◽  
Case Studies ◽  
Northern Nigeria ◽  
Support Structures ◽  
Muslim Community ◽  
Community Structures ◽  
Religious Identities ◽  
Ethnic Affiliation ◽  
Conversion To Islam

AbstractBefore the civil war, conversion to Islam for Igbo men resident in the predominantly Hausa city of Kano in northern Nigeria usually meant becoming Hausa. More recent converts, however, have retained their Igbo identity and created an organisation, the Igbo Muslim Community. Three case studies from the first group detail the process and criteria of becoming Hausa, including immersion in Hausa economic and social networks; three case studies from the second group demonstrate that, while Hausa-centred networks remain important, converts have worked to construct new, Igbo-centred support structures. The watershed in the changing relationship between religious and ethnic affiliation for Igbo converts is the end of the war in 1970 and resultant changes in Igbo perceptions of Muslims, and changes in Igbo community structures.

The Contribution of University Business Incubators to New Knowledge-Based Ventures

2001 ◽  
Vol 15 (4) ◽  
pp. 239-250 ◽  
Author(s):  
Rosa Grimaldi ◽  
Alessandro Grandi
Keyword(s):  
Case Studies ◽  
Empirical Analysis ◽  
Technological Knowledge ◽  
Business Incubators ◽  
Knowledge Based ◽  
New Knowledge ◽  
The Creation ◽  
The Empirical Analysis

This paper examines the role of university business incubators (UBIs) in supporting the creation of new knowledge-based ventures. UBIs are described as effective mechanisms for overcoming weaknesses of the more traditional public incubating institutions. They offer firms a range of university-related benefits, such as access to laboratories and equipment, to scientific and technological knowledge and to networks of key contacts, and the reputation that accrues from affiliation with a university. The empirical analysis is based on the Turin Polytechnic Incubator (TPI) and on case studies of six academic spin-offs hosted at TPI. While TPI does not effectively resolve such problems as inadequate access to funding capital and the lack of management and financial skills in its tenant companies, the networking capacity of incubating programmes is seen as a key characteristic that may help new knowledge-based ventures to overcome such difficulties.

Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

2016 ◽  
Vol 30 (16) ◽  
pp. 1650092 ◽  
Author(s):  
Tingting Wang ◽  
Weidi Dai ◽  
Pengfei Jiao ◽  
Wenjun Wang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Community Structures ◽  
Block Model ◽  
Real World Data ◽  
Stochastic Block Model ◽  
Dynamic Social Networks ◽  
Influential Nodes

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.

scholarly journals Potential Impacts of Foodborne Illness Incidences on Market Movements and Prices of Fresh Produce in the U.S.

2010 ◽  
Vol 42 (4) ◽  
pp. 731-741 ◽  
Author(s):  
Marco A. Palma ◽  
Luis A. Ribera ◽  
David Bessler ◽  
Mechel Paggi ◽  
Ronald D. Knutson
Keyword(s):  
Food Safety ◽  
Case Studies ◽  
Empirical Analysis ◽  
Vector Autoregression ◽  
Fresh Produce ◽  
Foodborne Illness ◽  
Var Model ◽  
June July ◽  
The Empirical Analysis ◽  
The U.S

This study investigates the potential impacts of food safety outbreaks on domestic shipments, imports, and prices of the produce industry. Three case studies were analyzed to assess these potential impacts: the cantaloupe outbreak of March–April 2008, the spinach outbreak of September 2006, and the tomato outbreak of June–July 2008. Data-determined historical decompositions were conducted to provide a weekly picture of domestic shipment, import, and price fluctuation transmissions. The empirical analysis based on a vector autoregression (VAR) model showed differences in the results depending on the source of the outbreak (domestic vs. imported).

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