Article

Structure and evolution of online social networks

Authors:

Andrew TomkinsAuthors Info & Claims

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 611 - 617

https://doi.org/10.1145/1150402.1150476

Published: 20 August 2006 Publication History

Abstract

In this paper, we consider the evolution of structure within large online social networks. We present a series of measurements of two such networks, together comprising in excess of five million people and ten million friendship links, annotated with metadata capturing the time of every event in the life of the network. Our measurements expose a surprising segmentation of these networks into three regions: singletons who do not participate in the network; isolated communities which overwhelmingly display star structure; and a giant component anchored by a well-connected core region which persists even in the absence of stars.We present a simple model of network growth which captures these aspects of component structure. The model follows our experimental results, characterizing users as either passive members of the network; inviters who encourage offline friends and acquaintances to migrate online; and linkers who fully participate in the social evolution of the network.

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Index Terms

Structure and evolution of online social networks
1. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Conferences

KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2006

986 pages

ISBN:1595933395

DOI:10.1145/1150402

Conference Chair:
Tina Eliassi-Rad
LLNL
,
General Chair:
Lyle Ungar
University of Pennsylvania
,
Program Chairs:
Mark Craven
University of Wisconsin
,
Dimitrios Gunopulos
University of California, Riverside

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 August 2006

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KDD06

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KDD06: The 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 20 - 23, 2006

PA, Philadelphia, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.32362/2500-316X-2024-12-3-78-92
Ai YXie XMa X(2024)Graph Contrastive Learning for Tracking Dynamic Communities in Temporal NetworksIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33868448:5(3422-3435)Online publication date: Oct-2024
https://doi.org/10.1109/TETCI.2024.3386844
Yu CRen TLi WLiu HMa HZhao Y(2024)BL: An Efficient Index for Reachability Queries on Large GraphsIEEE Transactions on Big Data10.1109/TBDATA.2023.332721510:2(108-121)Online publication date: Apr-2024
https://doi.org/10.1109/TBDATA.2023.3327215
Adak ZÇetinkaya A(2024)Exploring Spreaders in a Retweet Network: A Case from the 2023 Kahramanmaraş Earthquake SequenceEmerging Trends and Applications in Artificial Intelligence10.1007/978-3-031-56728-5_40(481-492)Online publication date: 30-Apr-2024
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