Fast Quasi-biclique Mining with Giraph

Liu, Hsiao-Fei; Su, Chin Cheng; Chu, An-Chiang

doi:10.1109/bigdata.congress.2013.53

Cited by 8 publications

(3 citation statements)

References 17 publications

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“…Existing studies of quasi-bicliques focus on finding the maximum quasi-biclique [23,24,30,45]. There are some other studies which find subgraphs with a certain density and degree [29,33]. In this paper, we focus on 𝑘-biplex for reasons as discussed in Section 1 (i.e., 𝑘-biplex imposes strict enough requirements on connections within a subgraph, tolerates some disconnections, and satisfies the hereditary property).…”

Section: Related Workmentioning

confidence: 99%

Efficient Algorithms for Maximal k-Biplex Enumeration

Yu¹,

Cheng²,

Liu³

et al. 2021

Preprint

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Mining maximal subgraphs with cohesive structures from a bipartite graph has been widely studied. One important cohesive structure on bipartite graphs is 𝑘-biplex, where each vertex on one side disconnects at most 𝑘 vertices on the other side. In this paper, we study the maximal 𝑘-biplex enumeration problem which enumerates all maximal 𝑘-biplexes. Existing methods suffer from efficiency and/or scalability issues and have the time of waiting for the next output exponential w.r.t. the size of the input bipartite graph (i.e., an exponential delay). In this paper, we adopt a reverse search framework called bTraversal, which corresponds to a depth-first search (DFS) procedure on an implicit solution graph on top of all maximal 𝑘-biplexes. We then develop a series of techniques for improving and implementing this framework including (1) carefully selecting an initial solution to start DFS, (2) pruning the vast majority of links from the solution graph of bTraversal, and (3) implementing abstract procedures of the framework. The resulting algorithm is called iTraversal, which has its underlying solution graph significantly sparser than (around 0.1% of) that of bTraversal. Besides, iTraversal provides a guarantee of polynomial delay. Our experimental results on real and synthetic graphs, where the largest one contains one billion edges, show that our algorithm is up to four orders of magnitude faster than existing algorithms. CCS CONCEPTS• Mathematics of computing → Graph algorithms.

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Section: Related Workmentioning

confidence: 99%

Efficient Algorithms for Maximal k-Biplex Enumeration

Yu¹,

Cheng²,

Liu³

et al. 2021

Preprint

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“…There have been few works connecting biclique mining and DNS security. For example, in [ 43 ], the authors suggest to use a DNS bipartite graph (like our graph model as presented in Section 2.4 ) to detect anomalies. Similarly, in [ 44 ], the authors propose to use social structures to detect DDoS attack on DNS server, and present some insights about this approach.…”

Section: Related Workmentioning

confidence: 99%

Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers

Alonso

Monroy

Trejo

2016

Sensors

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The Domain Name System (DNS) is a critical infrastructure of any network, and, not surprisingly a common target of cybercrime. There are numerous works that analyse higher level DNS traffic to detect anomalies in the DNS or any other network service. By contrast, few efforts have been made to study and protect the recursive DNS level. In this paper, we introduce a novel abstraction of the recursive DNS traffic to detect a flooding attack, a kind of Distributed Denial of Service (DDoS). The crux of our abstraction lies on a simple observation: Recursive DNS queries, from IP addresses to domain names, form social groups; hence, a DDoS attack should result in drastic changes on DNS social structure. We have built an anomaly-based detection mechanism, which, given a time window of DNS usage, makes use of features that attempt to capture the DNS social structure, including a heuristic that estimates group composition. Our detection mechanism has been successfully validated (in a simulated and controlled setting) and with it the suitability of our abstraction to detect flooding attacks. To the best of our knowledge, this is the first time that work is successful in using this abstraction to detect these kinds of attacks at the recursive level. Before concluding the paper, we motivate further research directions considering this new abstraction, so we have designed and tested two additional experiments which exhibit promising results to detect other types of anomalies in recursive DNS servers.

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“…and ( 2) 𝛾-quasi-biclique [47,53] is a bipartite graph 𝐺 (𝐿, 𝑅, 𝐸) that can miss at most ). Existing works of quasi-bicliques focus on finding subgraphs with a certain density and degree [54,55]. In summary, we compare the above cohesive models, in terms of (1) structure cohesiveness and (2) computational efficiency of solutions for finding cohesive subgraphs.…”

Section: Chapter 2 Literature Reviewmentioning

confidence: 99%

Cohesive subgraph mining in large graphs

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I would like to express my sincere gratitude to my supervisor, Prof. Cheng Long, for his continuous support and patient guidance throughout my Ph.D studies. Specifically, I feel extraordinarily grateful to Cheng for training me the skills of approaching research problems, writing papers and making presentation, providing invaluable feedbacks and suggestions constantly, and encouraging me to overcome the difficulties in my research.Far beyond that, his brilliant insights and intense enthusiasm have greatly shaped my attitude towards research. I am forever thankful for these help from Cheng.

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Fast Quasi-biclique Mining with Giraph

Cited by 8 publications

References 17 publications

Efficient Algorithms for Maximal k-Biplex Enumeration

Efficient Algorithms for Maximal k-Biplex Enumeration

Mining IP to Domain Name Interactions to Detect DNS Flood Attacks on Recursive DNS Servers

Cohesive subgraph mining in large graphs

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