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Fully dynamic algorithm for top-k densest subgraphs
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2017 (English)In: CIKM '17 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Association for Computing Machinery (ACM), 2017, p. 1817-1826Conference paper (Refereed)
Abstract [en]

Given a large graph, the densest-subgraph problem asks to find a subgraph with maximum average degree. When considering the top-k version of this problem, a nattive solution is to iteratively find the densest subgraph and remove it in each iteration. However, such a solution is impractical due to high processing cost. The problem is further complicated when dealing with dynamic graphs, since adding or removing an edge requires re-running the algorithm. In this paper, we study the top-k densest-subgraph problem in the sliding-window model and propose an efficient fully-dynamic algorithm. The input of our algorithm consists of an edge stream, and the goal is to find the node-disjoint subgraphs that maximize the sum of their densities. In contrast to existing state-of-the-art solutions that require iterating over the entire graph upon any update, our algorithm profits from the observation that updates only affect a limited region of the graph. Therefore, the top-k densest subgraphs are maintained by only applying local updates. We provide a theoretical analysis of the proposed algorithm and show empirically that the algorithm offen generates denser subgraphs than state-of-the-art competitors. Experiments show an improvement in efficiency of up to five orders of magnitude compared to state-of-the-art solutions.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017. p. 1817-1826
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-220424DOI: 10.1145/3132847.3132966Scopus ID: 2-s2.0-85037375605ISBN: 9781450349185 OAI: oai:DiVA.org:kth-220424DiVA, id: diva2:1168315
Conference
26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Pan Pacific Singapore HotelSingapore, Singapore, 6 November 2017 through 10 November 2017
Note

QC 20171220

Available from: 2017-12-20 Created: 2017-12-20 Last updated: 2018-04-05Bibliographically approved
In thesis
1. Mining Big and Fast Data: Algorithms and Optimizations for Real-Time Data Processing
Open this publication in new window or tab >>Mining Big and Fast Data: Algorithms and Optimizations for Real-Time Data Processing
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In the last decade, real-time data processing has attracted much attention from both academic community and industry, as the meaning of big data has evolved to incorporate as well the speed of data. The massive and rapid production of data comes via numerous services, i.e., Web, social networks, Internet of Things (IoT) and mobile devices. For instance, global positioning systems are producing continuous data points using various location-based services. IoT devices are continuously monitoring variety of parameters, like temperature, heart beats, and others, and sending the data over the network. Moreover, part of the data produced by these real-time services is linked-data that requires tools for streaming graph analytics. Real-time graphs are ubiquitous in many fields, from the web advertising to bio-analytics. Developing analytical tools to process this amount of information at a real-time is challenging, yet extremely essential, for developing new services in areas such as web analytics, e-health and marketing.

Distributed stream processing engines (dspes) are often employed for real-time data processing, as they distribute work to many machines to achieve the required performance guarantees, i.e., low latency and high throughput. However, the scalability of dspes is often questioned when the input streams are skewed or the underlying resources are heterogeneous. In this thesis, we perform a scalability study for dspes. In particular, we study the load- balancing problem for dspes, which is caused by the skewness in the workload and heterogeneity in the cluster. In doing so, we develop several efficient and accurate algorithms to reduce the load imbalance in a distributed system. Moreover, our algorithms are integrated into Apache Storm, which is an open source stream processing framework.

Another dimension of real-time data processing involves developing novel algorithms for graph-related problems. The later part of the thesis presents several algorithms for evolving graphs. One of the most interesting features of real-world networks is the presence of community structure, which divides a network into groups of nodes with dense connections internally and sparse connections between groups. We study the community detection problem in the fully dynamic settings by formulating it as a top-k densest subgraph problem. In doing so, we achieve an extremely efficient approximation algorithm that scales to graphs with billions of edges. Further, we study the top-k graph pattern-mining problem in fully dynamic settings and develop a probabilistic algorithm using reservoir sampling. We provide the theoretical analysis for the proposed algorithms and show via empirical evaluation that our algorithms achieve up to several orders of magnitude improvement compared to the state-of-the-art algorithm.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2018. p. 42
Series
TRITA-EECS-AVL ; 2018:27
Keywords
Stream Processing, Load Balancing, Fully Dynamic Graphs, Real-Time Data Processing, Top-k Densest Subgraph, Frequent Subgraph Mining
National Category
Computer Systems
Research subject
Information and Communication Technology; Computer Science
Identifiers
urn:nbn:se:kth:diva-225487 (URN)978-91-7729-729-1 (ISBN)
Public defence
2018-05-07, Sal B, Electrum building, Kistagången 16, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

QC 20180409

Available from: 2018-04-09 Created: 2018-04-05 Last updated: 2018-04-10Bibliographically approved

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Citation style
  • apa
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