The nature of statistical learning theory
The nature of statistical learning theory
Automatic document metadata extraction using support vector machines
Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries
Exploring the community structure of newsgroups
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
On six degrees of separation in DBLP-DB and more
ACM SIGMOD Record
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Capital in Friendship-Event Networks
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Identification and evaluation of weak community structures in networks
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Efficient name disambiguation for large-scale databases
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Discovering leaders from community actions
Proceedings of the 17th ACM conference on Information and knowledge management
A geographical analysis of knowledge production in computer science
Proceedings of the 18th international conference on World wide web
Characterizing the evolution of collaboration network
Proceedings of the 2nd ACM workshop on Social web search and mining
Boosting social network connectivity with link revival
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Extraction, characterization and utility of prototypical communication groups in the blogosphere
ACM Transactions on Information Systems (TOIS)
Analysis of computer science communities based on DBLP
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Finding potential research collaborators in four degrees of separation
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Evolution of developer collaboration on the jazz platform: a study of a large scale agile project
Proceedings of the 4th India Software Engineering Conference
CollabSeer: a search engine for collaboration discovery
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
The social network of software engineering research
Proceedings of the 5th India Software Engineering Conference
Predicting aggregate social activities using continuous-time stochastic process
Proceedings of the 21st ACM international conference on Information and knowledge management
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Degree distributions of evolving alphabetic bipartite networks and their projections
Theoretical Computer Science
The role of research leaders on the evolution of scientific communities
Proceedings of the 22nd international conference on World Wide Web companion
sonLP: social network link prediction by principal component regression
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Indirect weighted association rules mining for academic network collaboration recommendations
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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A formal type of scientific and academic collaboration is coauthorship which can be represented by a coauthorship network. Coauthorship networks are among some of the largest social networks and offer us the opportunity to study the mechanisms underlying large-scale real world networks. We construct such a network for the Computer Science field covering research collaborations from 1980 to 2005, based on a large dataset of 451,305 papers authored by 283,174 distinct researchers. By mining this network, we first present a comprehensive study of the network statistical properties for a longitudinal network at the overall network level as well as for the intermediate community level. Major observations are that the database community is the best connected while the AI community is the most assortative, and that the Computer Science field as a whole shows a collaboration pattern more similar to Mathematics than to Biology. Moreover, the small world phenomenon and the scale-free degree distribution accompany the growth of the network. To study the individual collaborations, we propose a novel stochastic model, Stochastic Poisson model with Optimization Tree (Spot)to efficiently predict any increment of collaboration based on the local neighborhood structure. Spot models the non-stationary Poisson process by maximizing the log-likelihood with a tree structure. Empirical results show that Spot outperforms Support Vector Regression by better fitting collaboration records and predicting the rate of collaboration