Clearing Contamination in Large Networks

Simpson, Michael; Srinivasan, Venkatesh; Thomo, Alex

doi:10.1109/tkde.2016.2525993

Cited by 11 publications

(1 citation statement)

References 27 publications

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“…However, it requires superlinear time, and hence it may not be suitable for very large graphs. Finding a minimum FAS has many additional applications beyond Graph Drawing, including misinformation removal, label propagation, and many application domains motivated by Social Network Analysis [6,9,16].…”

Section: Introductionmentioning

confidence: 99%

Computing a Feedback Arc Set Using PageRank

Geladaris¹,

Lionakis²,

Tollis³

2022

Preprint

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We present a new heuristic algorithm for computing a minimum Feedback Arc Set in directed graphs. The new technique produces solutions that are better than the ones produced by the best previously known heuristics, often reducing the FAS size by more than 50%. It is based on computing the PageRank score of the nodes of the directed line graph of the input directed graph. Although the time required by our heuristic is heavily influenced by the size of the produced line graph, our experimental results show that it runs very fast even for very large graphs used in graph drawing.

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Section: Introductionmentioning

confidence: 99%

Computing a Feedback Arc Set Using PageRank

Geladaris¹,

Lionakis²,

Tollis³

2022

Preprint

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Triangle Enumeration on Massive Graphs Using AWS Lambda Functions

Srinivasan

Thomo

2020

Advances in Intelligent Networking and Collaborative Systems

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Triangle enumeration is a fundamental task in graph data analysis with many applications. Recently, Park et al. proposed a distributed algorithm, PTE (Pre-partitioned Triangle Enumeration), that, unlike previous works, scales well using multiple high end machines and can handle very large real-world networks.This work presents a serverless implementation of the PTE algorithm using the AWS Lambda platform. Our experiments take advantage of the high concurrency of the Lambda instances to compete with the expensive server-based experiments of Park et al. Our analysis shows the trade-off between the time and cost of triangle enumeration and the numbers of tasks generated by the distributed algorithm. Our results reveal the importance of using a higher number of tasks in order to improve the efficiency of PTE. Such an analysis can only be performed using a large number of workers which is indeed possible using AWS Lambda but not easy to achieve using few servers as in the case of Park et al.

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