- Author:
- Alan E. Mislove,
- Adviser:
- Peter Druschel
Recently, online social networking sites have exploded in popularity. Numerous sites are dedicated to finding and maintaining contacts and to locating and sharing different types of content. Online social networks represent a new kind of information network that differs significantly from existing networks like the Web. For example, in the Web, hyperlinks between content form a graph that is used to organize, navigate, and rank information. The properties of the Web graph have been studied extensively, and have lead to useful algorithms such as PageRank. In contrast, few links exist between content in online social networks and instead, the links exist between content and users, and between users themselves. However, little is known in the research community about the properties of online social network graphs at scale, the factors that shape their structure, or the ways they can be leveraged in information systems.
In this thesis, we use novel measurement techniques to study online social networks at scale, and use the resulting insights to design innovative new information systems. First, we examine the structure and growth patterns of online social networks, focusing on how users are connecting to one another. We conduct the first large-scale measurement study of multiple online social networks at scale, capturing information about over 50 million users and 400 million links. Our analysis identifies a common structure across multiple networks, characterizes the underlying processes that are shaping the network structure, and exposes the rich community structure.
Second, we leverage our understanding of the properties of online social networks to design new information systems. Specifically, we build two distinct applications that leverage different properties of online social networks. We present and evaluate Ostra, a novel system for preventing unwanted communication that leverages the difficulty in establishing and maintaining relationships in social networks. We also present, deploy, and evaluate PeerSpective, a system for enhancing Web search using the natural community, structure in social networks. Each of these systems has been evaluated on data from real online social networks or in a deployment with real users.
Cited By
Rokka Chhetri S, Goyal P and Canedo A Tracking Temporal Evolution of Graphs using Non-Timestamped Data Proceedings of the 30th ACM Conference on Hypertext and Social Media, (173-180)- Matam K, Koo G, Zha H, Tseng H and Annavaram M GraphSSD Proceedings of the 46th International Symposium on Computer Architecture, (116-128)
- Bayrak A and Polat F Mining individual features to enhance link prediction efficiency in location based social networks Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (920-925)
- Baswana S, Goel A and Khan S Incremental DFS algorithms Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, (53-72)
- Fang Y, Cheng R, Chen Y, Luo S and Hu J (2017). Effective and efficient attributed community search, The VLDB Journal — The International Journal on Very Large Data Bases, 26:6, (803-828), Online publication date: 1-Dec-2017.
- Jiao L, Li J, Xu T, Du W and Fu X (2016). Optimizing cost for online social networks on geo-distributed clouds, IEEE/ACM Transactions on Networking, 24:1, (99-112), Online publication date: 1-Feb-2016.
- Liu Q, Tang S, Zhang X, Zhao X, Zhao B and Zheng H Network Growth and Link Prediction Through an Empirical Lens Proceedings of the 2016 Internet Measurement Conference, (1-15)
- Lin Y, Chen X and Lui J I/O Efficient Algorithms for Exact Distance Queries on Disk-Resident Dynamic Graphs Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, (440-447)
- Miao Y, Han W, Li K, Wu M, Yang F, Zhou L, Prabhakaran V, Chen E and Chen W (2015). ImmortalGraph, ACM Transactions on Storage, 11:3, (1-34), Online publication date: 29-Jul-2015.
- Han W, Miao Y, Li K, Wu M, Yang F, Zhou L, Prabhakaran V, Chen W and Chen E Chronos Proceedings of the Ninth European Conference on Computer Systems, (1-14)
- Akiba T, Iwata Y and Yoshida Y Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling Proceedings of the 23rd international conference on World wide web, (237-248)
- Bhat S and Abulaish M (2013). Analysis and mining of online social networks, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3:6, (408-444), Online publication date: 1-Nov-2013.
- Kunegis J, Fay D and Bauckhage C (2013). Spectral evolution in dynamic networks, Knowledge and Information Systems, 37:1, (1-36), Online publication date: 1-Oct-2013.
- Kunegis J, Blattner M and Moser C Preferential attachment in online networks Proceedings of the 5th Annual ACM Web Science Conference, (205-214)
- Zheng Q, Zhu P, Wang Y and Xu M EPSP Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing, (578-583)
- Kunegis J, Fay D and Bauckhage C Network growth and the spectral evolution model Proceedings of the 19th ACM international conference on Information and knowledge management, (739-748)
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