On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Densification arising from sampling fixed graphs
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Photo-based authentication using social networks
Proceedings of the first workshop on Online social networks
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A brief survey on anonymization techniques for privacy preserving publishing of social network data
ACM SIGKDD Explorations Newsletter
Networks, communities and kronecker products
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Privacy challenges and solutions in the social web
Crossroads - The Social Web
News Posting by Strategic Users in a Social Network
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Kronecker Graphs: An Approach to Modeling Networks
The Journal of Machine Learning Research
Measurement-calibrated graph models for social network experiments
Proceedings of the 19th international conference on World wide web
WAW'07 Proceedings of the 5th international conference on Algorithms and models for the web-graph
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A random walk approach to modeling the dynamics of the blogosphere
NETWORKING'11 Proceedings of the 10th international IFIP TC 6 conference on Networking - Volume Part I
Inferring Networks of Diffusion and Influence
ACM Transactions on Knowledge Discovery from Data (TKDD)
Giant components in Kronecker graphs
Random Structures & Algorithms
A differentially private estimator for the stochastic Kronecker graph model
Proceedings of the 2012 Joint EDBT/ICDT Workshops
On certain properties of random apollonian networks
WAW'12 Proceedings of the 9th international conference on Algorithms and Models for the Web Graph
Degree relations of triangles in real-world networks and graph models
Proceedings of the 21st ACM international conference on Information and knowledge management
Evolution of social-attribute networks: measurements, modeling, and implications using google+
Proceedings of the 2012 ACM conference on Internet measurement conference
An in-depth analysis of stochastic Kronecker graphs
Journal of the ACM (JACM)
Modeling Social Network Interaction Graphs
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Solving the missing node problem using structure and attribute information
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Analyzing future communities in growing citation networks
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
Anonymizing social networks: A generalization approach
Computers and Electrical Engineering
Effective immunization of online networks: a self-similar selection approach
Information Technology and Management
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Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small) diameter, similar spectrum, etc? We propose to use "Kronecker graphs", which naturally obey all of the above properties, and we present KronFit, a fast and scalable algorithm for fitting the Kronecker graph generation model to real networks. A naive approach to fitting would take super-exponential time. In contrast, KronFit takes linear time, by exploiting the structure of Kronecker product and by using sampling. Experiments on large real and synthetic graphs show that KronFit indeed mimics very well the patterns found in the target graphs. Once fitted, the model parameters and the resulting synthetic graphs can be used for anonymization, extrapolations, and graph summarization.