Partitioning sparse matrices with eigenvectors of graphs
SIAM Journal on Matrix Analysis and Applications
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Finding Frequent Items in Data Streams
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Frequency Estimation of Internet Packet Streams with Limited Space
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
What's hot and what's not: tracking most frequent items dynamically
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2003
The political blogosphere and the 2004 U.S. election: divided they blog
Proceedings of the 3rd international workshop on Link discovery
An integrated efficient solution for computing frequent and top-k elements in data streams
ACM Transactions on Database Systems (TODS)
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing influence in a competitive social network: a follower's perspective
Proceedings of the ninth international conference on Electronic commerce
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Efficient semi-streaming algorithms for local triangle counting in massive graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Word of Mouth: Rumor Dissemination in Social Networks
SIROCCO '08 Proceedings of the 15th international colloquium on Structural Information and Communication Complexity
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Efficient influence maximization in social networks
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Community detection using a measure of global influence
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Limiting the spread of misinformation in social networks
Proceedings of the 20th international conference on World wide web
Interaction-driven opinion dynamics in online social networks
Proceedings of the First Workshop on Social Media Analytics
Where the blogs tip: connectors, mavens, salesmen and translators of the blogosphere
Proceedings of the First Workshop on Social Media Analytics
Structural trend analysis for online social networks
Proceedings of the VLDB Endowment
Efficient identification of overlapping communities
ISI'05 Proceedings of the 2005 IEEE international conference on Intelligence and Security Informatics
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With hundreds of millions of users worldwide, social networks provide incredible opportunities for social connection, learning, political and social change, and individual entertainment and enhancement in a wide variety of forms. In light of these notable outcomes, understanding information diffusion over online social networks is a critical research goal. Because many social interactions currently take place in online networks, we now have have access to unprecedented amounts of information about social interaction. Prior to the advent of such online networks, investigations about social behavior required resource-intensive activities such as random trials, surveys, and manual data collection to gather even small data sets. Now, massive amounts of information about social networks and social interactions are recorded. This wealth of data can allow us to study social interactions on a scale and at a level of detail that has never before been possible. We present an integrated approach to information diffusion in online social networks focusing on three key problems: (1) Querying and analysis of online social network datasets; (2) Modeling and analysis of social networks; and (3) Analysis of social media and social interactions in the contemporary media environment. The overarching goals are to generate a greater understanding of social interactions in online networks through data analysis, to develop reliable and scalable models that can predict outcomes of these social processes, and ultimately to create applications that can shape the outcome of these processes. We start by developing and refining models of information diffusion based on realworld data sets. We next address the problem of finding influential users in this data-driven framework. It is equally important to identify techniques that can slow or prevent the spread of misinformation, and hence algorithms are explored to address this question. A third interest is the process by which a social group forms opinions about an idea or product, and we therefore describe preliminary approaches to create models that accurately capture the opinion formation process in online social networks. While questions relating to the propagation of a single news item or idea are important, these information campaigns do not exist in isolation. Therefore, our proposed approach also addresses the interplay of the many information diffusion processes that take place simultaneously in a network and the relative importance of different topics or trends over multiple spatial and temporal resolutions.