Jian Liu

Research Interests

• Model reduction of complex networks
• Chemical reaction kinetics
• Numerical solution of SDEs

Phd Thesis

• My Phd Thesis (in Chinese) can be downloaded here.
• The Slides (in Chinese) for my defense can be downloaded here.

Publications

• Refereed Journals

  • Jian Liu and Tiejun Li, A validity index approach for network partitions, Physica A: Statistical Mechanics and its Applications, 390 (2011), 3579-3591.
  • Jian Liu and Tingzhan Liu, Coarse-grained diffusion distance for community structure detection in complex networks, Journal of Statistical Mechanics: Theory and Experiment, (2010), P12030.
  • Jian Liu, Fuzzy modularity and fuzzy community structure in networks, The European Physical Journal B: Condensed Matter and Complex Systems, 77 (2010), 547-557.
  • Jian Liu and Tingzhan Liu, Detecting community structure in complex networks using simulated annealing with k-means algorithms, Physica A: Statistical Mechanics and its Applications, 389 (2010), 2300-2309.
  • Jian Liu, Detecting the fuzzy clusters of complex networks, Pattern Recognition, 43 (2010), 1334-1345.
  • Tiejun Li, Jian Liu and Weinan E, Probabilistic framework for network partition, Physical Review E, 80 (2009), 026106.

• Conference Proceedings

  • Jian Liu, Comparative analysis for k-means algorithms in network community detection, Lecture Notes in Computer Science, 6382 (2010), 158-169.
  • Jian Liu, Comparing fuzzy algorithms on overlapping communities in networks, Lecture Notes in Computer Science, 6377 (2010), 269-276.
  • Jian Liu, Fuzzy algorithm based on diffusion maps for network partition, Lecture Notes in Artificial Intelligence, 6216 (2010), 163-172.
  • Jian Liu, Finding and evaluating fuzzy clusters in networks, Lecture Notes in Computer Science, 6146 (2010), 17-26.
  • Jian Liu, An extended validity index for identifying community structure in networks, Lecture Notes in Computer Science, 6064 (2010), 258-267.
  • Jian Liu and Na Wang, Detecting community structure of complex networks by affinity propagation, Proceedings of 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, 4 (2009), 13-19.
  • Jian Liu and Na Wang, Detecting community structure of complex networks by simulated annealing with optimal prediction, Proceedings of International Conference on Computational Intelligence and Software Engineering, 2 (2009).

Network Data

• Generation for Artificial Networks

  • LFR benchmark. Methods to detect communities in graphs need to be thoroughly tested. To do that, one needs benchmark graphs with a built-in community structure, that the methods should identify. Standard benchmarks, like that by Girvan and Newman, do not account for important features of real networks, like the fat-tailed distributions of node degree and community size. Therefore, new classes of benchmark graphs, in which the distributions of node degree and community size are both power laws with tunable exponents, have been proposed. The code to build the benchmark for the simplest case of undirected and unweighted graphs can be downloaded here.

  • LFR benchmark with overlapping communities. The code is written in C++. In each package there is a Readme file where you can find instructions on how to use the code and simple examples.
    • Package 1 includes the code to generate undirected and unweighted graphs with overlapping communities. The extent of the overlap can be tuned by input, if there is no overlap the graphs are the same as those produced by the previous package.
    • Package 2 includes the code to generate undirected weighted graphs with possibly overlapping communities.
    • Package 3 includes the code to generate directed unweighted graphs with possibly overlapping communities.
    • Package 4 includes the code to generate directed weighted graphs with possibly overlapping communities.
    • Package 5 includes the code to generate the hierarchical benchmarks, with communities inside other communities.

• Data of Real World Networks

  • Newman's data sets. The data sets are in GML format. For a description of GML see here. A simple parser in C that will read the files into a data structure is available here. There are many features of GML not supported by this parser, but it will read the files in this repository just fine.
    • Zachary's karate club: Social network of friendships between 34 members of a karate club at a US university in the 1970s.
    • Dolphin social network: An undirected social network of frequent associations between 62 dolphins in a community living off Doubtful Sound, New Zealand.
    • Books about US politics: A network of books about US politics published around the time of the 2004 presidential election and sold by the online bookseller Amazon.com. Edges between books represent frequent copurchasing of books by the same buyers.
    • Les Miserables: Coappearance network of characters in the novel Les Miserables.
    • Word adjacencies: Adjacency network of common adjectives and nouns in the novel David Copperfield by Charles Dickens.
    • American College football: A network of American football games between Division IA colleges during regular season Fall 2000.
    • SFI collaboration: Collaboration network of 271 scientists at the Santa Fe Institute, an interdisciplinary research center in Santa Fe, New Mexico, with the largest component consisting of 118 scientists.

  • Other sources of network data. There are a number of other pages on the web from which you can download network data.
    • UCINet data sets: Social network data sets released with the UCINet software by Steve Borgatti et al.
    • Indiana University data sets: A set of very large data sets, including some non-network data sets, compiled by the School of Library and Information Science at Indiana University. Network data sets include the NBER data set of US patent citations and a data set of links between articles in the on-line encyclopedia Wikipedia.
    • Duncan Watts' data sets: Data compiled by Prof. Duncan Watts and collaborators at Columbia University, including data on the structure of the Western States Power Grid and the neural network of the worm C. Elegans.
    • Laszlo Barabasi's data sets: Data compiled by Prof. Albert-Laszlo Barabasi and collaborators at the University of Notre Dame, including web data and biochemical networks.
    • Alex Arenas's data sets: Data compiled by Prof. Alexandre Arenas and collaborators at Universidad Rovira i Virgili, including metabolic network data and the network from their study of the collaboration patterns of jazz musicians.

Useful Links

Last modified: July 1, 2011