On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
A random graph model for massive graphs
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
A Functional Approach to External Graph Algorithms
ESA '98 Proceedings of the 6th Annual European Symposium on Algorithms
Nexus: Small Worlds and the Groundbreaking Theory of Networks
Nexus: Small Worlds and the Groundbreaking Theory of Networks
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Fast Frequent Free Tree Mining in Graph Databases
World Wide Web
Graph OLAP: Towards Online Analytical Processing on Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Temporal mining for interactive workflow data analysis
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via latent social dimensions
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the Temporal Dimension of the Information Propagation
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Characterizing user behavior in online social networks
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
DETECTION OF SOCIAL INTERACTION IN SMART SPACES
Cybernetics and Systems - SOCIAL AWARENESS IN SMART SPACES: PART I
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph structures and algorithms for query-log analysis
CiE'10 Proceedings of the Programs, proofs, process and 6th international conference on Computability in Europe
Modeling Information Diffusion in Implicit Networks
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Foundations of Multidimensional Network Analysis
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Finding and Characterizing Communities in Multidimensional Networks
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Scalable Link Prediction on Multidimensional Networks
ICDMW '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops
Hi-index | 0.00 |
Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks.