An efficient algorithm for approximate betweenness centrality computation

In network analysis, the betweenness centrality of a node informally captures\nthe fraction of shortest paths visiting that node. The computation of the\nbetweenness centrality measure is a fundamental task in the analysis of modern\nnetworks, enabling the identification of the most central nodes in such\nnetworks. Additionally to being massive, modern networks also contain\ninformation about the time at which their events occur. Such networks are often\ncalled temporal networks. The temporal information makes the study of the\nbetweenness centrality in temporal networks (i.e., temporal betweenness\ncentrality) much more challenging than in static networks (i.e., networks\nwithout temporal information). Moreover, the exact computation of the temporal\nbetweenness centrality is often impractical on even moderately-sized networks,\ngiven its extremely high computational cost. A natural approach to reduce such\ncomputational cost is to obtain high-quality estimates of the exact values of\nthe temporal betweenness centrality. In this work we present ONBRA, the first\nsampling-based approximation algorithm for estimating the temporal betweenness\ncentrality values of the nodes in a temporal network, providing rigorous\nprobabilistic guarantees on the quality of its output. ONBRA is able to compute\nthe estimates of the temporal betweenness centrality values under two different\noptimality criteria for the shortest paths of the temporal network. In\naddition, ONBRA outputs high-quality estimates with sharp theoretical\nguarantees leveraging on the \\emph{empirical Bernstein bound}, an advanced\nconcentration inequality. Finally, our experimental evaluation shows that ONBRA\nsignificantly reduces the computational resources required by the exact\ncomputation of the temporal betweenness centrality on several real world\nnetworks, while reporting high-quality estimates with rigorous guarantees.\n

In 2020, the world is facing a Covid-19 virus pandemic. The fields of epidemiology and networks are needed in dealing with its spread. Individual (contact) tracing is an important control measure in the spread of infectious diseases. The network of contacts describes the potential pathways for the spread of the disease. Social Network Analysis (SNA) is the study of structural construction based on graph theory with Degree Centrality, Betweenness Centrality, and Closeness Centrality. In this paper, the SNA graph provides a description of the contact network in a resident cluster. Visualization of the formed SNA graph provides information on the pattern of contact relationships in the cluster. The measurement of centrality in the SNA method identifies who has a high value of connectedness and closeness in the cluster. The calculation of degree centrality shows that node-F391 is the node with the highest interaction, which is 3,203. This indicates that node interacts the most with residents in its environment within 14 days before and after being positive for COVID-19 based on the date of the swab test. The calculation of Betweenness Centrality shows that nodes-B041 and C371 are important actors with the highest value of 0.006. The two node becomes an important link. The use of graphs on SNA provides visualization of the contact network to distinguish potential relationships between close contacts, so that contact tracing can be carried out based on disease path mapping. Compared to the conventional way of reviewing individual contact records, graph visualization on the SNA is a good adjunct tool for healthcare workers in contact tracing investigations.

We present a new lock-free parallel algorithm for computing betweenness centrality of massive complex networks that achieves better spatial locality compared with previous approaches. Betweenness centrality is a key kernel in analyzing the importance of vertices (or edges) in applications ranging from social networks, to power grids, to the influence of jazz musicians, and is also incorporated into the DARPA HPCS SSCA#2, a benchmark extensively used to evaluate the performance of emerging high-performance computing architectures for graph analytics. We design an optimized implementation of betweenness centrality for the massively multithreaded Cray XMT system with the Thread-storm processor. For a small-world network of 268 million vertices and 2.147 billion edges, the 16-processor XMT system achieves a TEPS rate (an algorithmic performance count for the number of edges traversed per second) of 160 million per second, which corresponds to more than a 2× performance improvement over the previous parallel implementation. We demonstrate the applicability of our implementation to analyze massive real-world datasets by computing approximate betweenness centrality for the large IMDb movie-actor network.

Heuristic Algorithm for Approximation Betweenness Centrality Using Graph Coarsening

오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

Betweenness centrality is an important metric in the study of social networks, and several algorithms for computing this metric exist in the literature. This paper makes three contributions. First, we show that the problem of computing betweenness centrality can be formulated abstractly in terms of a small set of operators that update the graph. Second, we show that existing parallel algorithms for computing betweenness centrality can be viewed as implementations of different schedules for these operators, permitting all these algorithms to be formulated in a single framework. Third, we derive a new asynchronous parallel algorithm for betweenness centrality that (i) works seamlessly for both weighted and unweighted graphs, (ii) can be applied to large graphs, and (iii) is able to extract large amounts of parallelism. We implemented this algorithm and compared it against a number of publicly available implementations of previous algorithms on two different multicore architectures. Our results show that the new algorithm is the best performing one in most cases, particularly for large graphs and large thread counts, and is always competitive against other algorithms.

Querying large graphs in biomedicine with colored graphs and decomposition.

Betweenness centrality is one of the most well-known measures of the importance of nodes in a social-network graph. In this paper we describe the first known external-memory and cache-oblivious algorithms for computing betweenness centrality. We present four different external-memory algorithms exhibiting various tradeoffs with respect to performance. Two of the algorithms are cache-oblivious. We describe general algorithms for networks with weighted and unweighted edges and a specialized algorithm for networks with small diameters, as is common in social networks exhibiting the “small worlds” phenomenon.

Centrality indices are essential in network analysis and betweenness centrality, which is based on shortest paths, is one of the most important measures. It has been widely used in different areas like social network analysis, World Wide Web and route planning. However, even for mid-size networks, it is computationally expensive to compute exact betweenness scores. In this paper, we propose a generic randomized framework for unbiased estimation of betweenness scores. The proposed framework can be adapted with various sampling techniques and give algorithms with different characteristics. We discuss the conditions a promising sampling technique should satisfy to minimize the approximation error, and propose a sampling method that partially satisfies the conditions. We perform extensive experiments on synthetic networks as well as networks from the real world, and show that, compared with existing exact and inexact algorithms, our method works with higher accuracy or gives significant speedups.

A large network frequently evolves in nature; hence, it is necessary to update the betweenness centrality efficiently. Previously, the complexity for updated betweenness centrality is found \(O(n^3)\), where n is the number of the nodes in the network. It is hard to find a network with static size. To calculate, betweenness centrality for the evolving network is a nontrivial task due to high complexity. The betweenness centrality of all the nodes should be recomputed. Brendes proposed an algorithm for calculating updated betweenness centrality for the static networks with complexity, O(nm), where m is the total number of edges. A method QUBE was proposed which efficiently reduces the search space by finding candidate set of nodes and compute the betweenness centrality of the candidate nodes whose betweenness centrality need to be updated when any edge/node is inserted/deleted in the network. We have proposed a new algorithm which updates the betweenness centrality of a candidate set of nodes only with the approximation method. In the present investigations, a method is suggested to recompute the betweenness centrality for k number of nodes in the network, \((k < n)\), without actually recomputing the betweenness centrality for all the nodes. It is found that the time complexity of proposed algorithm is lower than the existing algorithms.

This study aimed to produce a network structure in Integrated Urban Farming Program in Bandung City to map the involved policy actors to realize a food-smart city. In this study, a mixed method was used with an exploratory sequential strategy involving policy actors from the government, private sector, academia, community, and mass media. To obtain a network structure in Integrated Urban Farming toward determining the most important actors, the Social Network Analysis (SNA) approach was also employed through the Gephi application. From this context, the structure emphasized four dimensions, namely Degree, Betweenness, Closeness, and Eigenvector Centralities. The results showed that the actor with the most connections (degree of centrality) and best communication control (betweenness centrality) is Parahyangan Catholic University (academic). At the same time, the actor that plays the most important role (eigenvector) is at the lower level of the government’s Sub-District and Urban Village. This study is useful for explaining the importance of the position of actors in the urban farming policy network, which is the key to the success of a program.

Betweenness is a centrality measure based on shortest paths, widely used in complex network analysis. It is computationally-expensive to exactly determine betweenness; currently the fastest-known algorithm by Brandes requires O(nm) time for unweighted graphs and O(nm + n2 log n) time for weighted graphs, where n is the number of vertices and m is the number of edges in the network. These are also the worstcase time bounds for computing the betweenness score of a single vertex. In this paper, we present a novel approximation algorithm for computing betweenness centrality of a given vertex, for both weighted and unweighted graphs. Our approximation algorithm is based on an adaptive sampling technique that significantly reduces the number of single-source shortest path computations for vertices with high centrality. We conduct an extensive experimental study on real-world graph instances, and observe that our random sampling algorithm gives very good betweenness approximations for biological networks, road networks and web crawls.

Betweenness centrality quantifies the importance of graph nodes in a variety of applications including social, biological and communication networks. Its computation is very costly for large graphs; therefore, many approximate methods have been proposed. Given the lack of a golden standard, the accuracy of most approximate methods is evaluated on tiny graphs and is not guaranteed to be representative of realistic datasets that are orders of magnitude larger. In this paper, we develop BeBeCA, a benchmark for betweenness centrality approximation methods on large graphs. Specifically: (i) We generate a golden standard by deploying a parallel implementation of Brandes algorithm using 96,000 CPU cores on a supercomputer to compute exact betweenness centrality values for several large graphs with up to 126M edges. (ii) We propose an evaluation methodology to assess various aspects of approximation accuracy, such as average error and quality of node ranking. (iii) We survey a large number of existing approximation methods and compare their performance and accuracy using our benchmark. (iv) We publicly share our benchmark, which includes the golden standard exact betweenness centrality values together with the scripts that implement our evaluation methodology; for researchers to compare their own algorithms and practitioners to select the appropriate algorithm for their application and data.

The use of online social media is also connected with the real world. A very common example of this is the effect of social media coverage on the chances of success of elections. Previous literature has identified that the outcome of elections can often be predicted based on online public discussions. These discussions can be across various online social network with a special focus on the candidate's own accounts. Among many other forms of social media, Wikipedia is a very widely-used self-organizing information resource. The management and administration of Wikipedia is performed using special users which are elected by means of online public elections. In other words, the results of these elections pose as an emergent outcome of a large-scale self-organized opinion formation process. However, due to dynamical, and non-linear interactions besides the presence of mutual dependencies between election participants, a statistical analysis of this data can both be cumbersome as well as inefficient in terms of information extraction. We believe that social network analysis is a more appropriate alternative. It allows for the identification of local and global patterns, identification of influential nodes as well as the contacts involved in the influence. In general, this particular analytic technique can help in examining the internal complex network dynamics. In the current paper, we investigates whether personal contacts matter more than know-how contacts in wiki election nominations and voting participation. We employ the use of standard social network analysis tools such as Pajek and Gephi. The presented work demonstrates the significance of personal contacts over know-how contacts of a person in online elections. We have discovered that personal contacts, i.e. immediate neighbors (based on degree centrality) and neighborhood (k-neighbors) of a person have a positive effect on a person’s nomination as an administrator and also contribute to the active participation of voters in voting. Moreover, know-how contacts, analyzed by means of measures such as betweenness and closeness centralities, have a relatively insignificant effect on the selection of a person. However, know-how contacts, measured in terms of betweenness centrality can positively contribute only to the voting process—primarily due to the role played in passing information around the network. These contacts, also measured in terms of influence domain and PageRank, can play a vital role in the selection of an admin. Additionally, such contacts have a positive association with the voting process in terms of reachability and brokerage roles.

코로나(Covid19) 사태로 인해 자동차 공급사슬에서의 단절(Disruption)로 인한 문제가 더욱 부각 되었고 무엇보다 안정적인 공급체인 구성이 기업의 생존에 화두가 되고 있다. 특히 복잡한 공급사슬 구조를 가져 유기적으로 연결되어 있는 자동차 부품산업은 각 공급업체별 개별적 분석으로 전체를 이해하는데 한계점이 존재한다. 따라서 좀 더 확장된 시각에서의 폭넓은 부품 네트워크의 실증적 분석에 대한 요구가 높아지고 있다. 본 연구에서는 한국자동차산업을 사회연결망분석(SNA_Social Network Analysis)기반하여 부품 네트워크를 분석하였다. 기존 연구에서는 각 개별 기업별 중심성(연결 중심성, 매개 중심성, 근접 중심성)을 특정 부품(브레이크 등)으로 국한하여 연구가 진행되었다. 본 연구에서는 자동차에 포함되는 부품들을 기능별로 구분하여 대부분의 주요 부품을 포함시켜서 각 부품의 한국자동차산업 네트워크의 특성을 분석하였다. 한국자동차사산업의 부품기능별로 체계적인 분석을 위하여 산업현장에서 통용되고 있는 분류체계 중에서 대한무역투자진흥공사(KOTRA)에서 수출입 자동차 부품 집계용으로 분류 사용하고 있는 6대 분류체계를 본 연구에 적용하였다. 이에 따른 연구 결과로 외향연결중심성(방향성 있음/없음)과 매개 중심성(방향성 없음)에서 부품기능별 유의미한 차이점을 확인할 수 있었다. 이를 통해 한국자동차산업에서의 부품기능별 부품 네트워크 특성 이해와 더불어 한국자동차산업 정책 전반에 참조할 수 있는 시사점을 제공하고자 하는데 본 연구의 목적이 있다.Understanding the automotive supply chain structure has received greater focus due to the disruption caused by Covid-19. Above all, a sustainable supply chain is now essential for companies’ survival. Particularly, the automobile parts industry, which has a complex supply chain structure, is difficult to understand through individual supplier analysis. Therefore, to provide better understanding for the structure of the auto parts supply chain, it is necessary to analyze the broad network. We analyzed the auto parts network of the Korean automobile industry using social network analysis (SNA). Previous studies considered the centrality of each individual company (degree centrality, betweenness centrality, closeness centrality) with specific parts (brakes, etc.). This study included the auto parts, classified by function and most major parts, and analyzed the characteristics of the network of the Korean automobile industry by part We found significant differences among part groups based on function with degree centrality and betweenness centrality. This study can provide the automobile industry policy suggestions through understanding of the characteristics of the Korean automobile parts network.