Scalable Online Betweenness Centrality in Evolving Graphs

Kourtellis, Nicolas; Morales, Gianmarco De Francisci; Bonchi, Francesco

doi:10.1109/tkde.2015.2419666

Cited by 78 publications

(68 citation statements)

References 28 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is especially desirable in the field of review spam detection, as reviews are constantly being added to the corpus. SAMOA has been used to analyze live Twitter streams [40], which involves similar text processing that can be applied to online reviews. Current research has largely ignored feature selection techniques in their experiments, even when using text features, which can potentially lead to highly dimensional feature sets.…”

Section: Comparative Analysis and Suggestionsmentioning

confidence: 99%

Survey of review spam detection using machine learning techniques

Crawford

Khoshgoftaar

Prusa

et al. 2015

Journal of Big Data

401

176

View full text Add to dashboard Cite

Online reviews are often the primary factor in a customer's decision to purchase a product or service, and are a valuable source of information that can be used to determine public opinion on these products or services. Because of their impact, manufacturers and retailers are highly concerned with customer feedback and reviews. Reliance on online reviews gives rise to the potential concern that wrongdoers may create false reviews to artificially promote or devalue products and services. This practice is known as Opinion (Review) Spam, where spammers manipulate and poison reviews (i.e., making fake, untruthful, or deceptive reviews) for profit or gain. Since not all online reviews are truthful and trustworthy, it is important to develop techniques for detecting review spam. By extracting meaningful features from the text using Natural Language Processing (NLP), it is possible to conduct review spam detection using various machine learning techniques. Additionally, reviewer information, apart from the text itself, can be used to aid in this process. In this paper, we survey the prominent machine learning techniques that have been proposed to solve the problem of review spam detection and the performance of different approaches for classification and detection of review spam. The majority of current research has focused on supervised learning methods, which require labeled data, a scarcity when it comes to online review spam. Research on methods for Big Data are of interest, since there are millions of online reviews, with many more being generated daily. To date, we have not found any papers that study the effects of Big Data analytics for review spam detection. The primary goal of this paper is to provide a strong and comprehensive comparative study of current research on detecting review spam using various machine learning techniques and to devise methodology for conducting further investigation.

show abstract

Section: Comparative Analysis and Suggestionsmentioning

confidence: 99%

Survey of review spam detection using machine learning techniques

Crawford

Khoshgoftaar

Prusa

et al. 2015

Journal of Big Data

401

176

View full text Add to dashboard Cite

show abstract

“…Existing methods can be categorized into three types: the network structure based methods, the user behavior based methods, and the mutual information based methods. The network structure based methods are degree centrality [22], closeness centrality [23], betweenness centrality [24], eigenvector centrality [25], Katz centrality [26], PageRank [27], and clustering coefficient [28]. We know that node degree essentially means the connection between a node and its neighbors.…”

Section: Related Workmentioning

confidence: 99%

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Liu

Lin

2017

Complexity

View full text Add to dashboard Cite

Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.

show abstract

“…E-mails: fuad.jamour@kaust.edu.sa and panos.kalnis@kaust.edu.sa • Spiros Skiadopoulos is with the University of the Peloponnese, Greece. E-mail: spiros@uop.gr Algorithm Time (sec) Space (GB) Cores Green [8] crashed 4,000.0 1 QUBE [9] 4,210 0.2 1 Lee 2016 1 [10] 2,634 0.06 1 Kourtellis [11] 2,376 4,000.0 100 iCENTRAL 190 1.6 20 TABLE 1: Performance of the best incremental algorithms for updating betweenness centrality after inserting one edge; twitter-munmun dataset (460K nodes, 833K edges).…”

Section: Introductionmentioning

confidence: 99%

“…This approach is faster than the original QUBE (refer to Lee 2016 in Table 1), but it is still slow, since it requires complete recomputation within each affected biconnected component. 3) Kourtellis' algorithm [11] is a parallel map-reduce variation of Green's algorithm that uses the Hadoop file system, instead of RAM, to store the all-pairs shortest path information. As shown in Table 1, Kourtellis needs 4TB of distributed disk space for the twitter-munmun graph, and finishes roughly 2 times faster than QUBE, but uses 100 cores whereas QUBE needs only one.…”

Section: Introductionmentioning

confidence: 99%

“…Other than some synchronization at the beginning and the end of the breadth-first searches, our implementation is embarrassingly parallel; therefore it scales well to large computer clusters. Parallel iCENTRAL can handle the largest graphs reported in previous work, using an order of magnitude less computational resources than the best existing parallel algorithm [11]. We make the following contributions in this paper:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

Jamour

Skiadopoulos

Kalnis

2018

IEEE Trans. Parallel Distrib. Syst.

View full text Add to dashboard Cite

Abstract-Betweenness centrality quantifies the importance of nodes in a graph in many applications, including network analysis, community detection and identification of influential users. Typically, graphs in such applications evolve over time. Thus, the computation of betweenness centrality should be performed incrementally. This is challenging because updating even a single edge may trigger the computation of all-pairs shortest paths in the entire graph. Existing approaches cannot scale to large graphs: they either require excessive memory (i.e., quadratic to the size of the input graph) or perform unnecessary computations rendering them prohibitively slow. We propose iCENTRAL; a novel incremental algorithm for computing betweenness centrality in evolving graphs. We decompose the graph into biconnected components and prove that processing can be localized within the affected components. iCENTRAL is the first algorithm to support incremental betweeness centrality computation within a graph component. This is done efficiently, in linear space; consequently, iCENTRAL scales to large graphs. We demonstrate with real datasets that the serial implementation of iCENTRAL is up to 3.7 times faster than existing serial methods. Our parallel implementation that scales to large graphs, is an order of magnitude faster than the state-of-the-art parallel algorithm, while using an order of magnitude less computational resources.

show abstract

Scalable Online Betweenness Centrality in Evolving Graphs

Cited by 78 publications

References 28 publications

Survey of review spam detection using machine learning techniques

Survey of review spam detection using machine learning techniques

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Parallel Algorithm for Incremental Betweenness Centrality on Large Graphs

Contact Info

Product

Resources

About