Statistical mechanics of complex networks
Authors: Reka Albert, Albert-Laszlo Barabasi
Comments: 54 pages, submitted to Reviews of Modern Physics
Subj-class: Statistical Mechanics; Disordered Systems and Neural Networks; Mathematical Physics; Data Analysis, Statistics and Probability; Adaptation and Self-Organizing Systems; Networking and Internet Architecture
Journal-ref: Reviews of Modern Physics 74, 47 (2002)
This dissertations presents an algorithm on the webgraph for finding dense bipartite graphs wich represents web-communities.
By performing further steps of the algorithm several levels of communities are recognized which can be related to communites of former levels.
The web can be represented by a graph with special regions: SCC, IN, OUT and TENDRILS.
Regions are defined by the link-path-reach from one website to others.
The linkage to and from a website (in- and out-degree) seems to conform the power law, which is also mentioned in this document.
A pre-relational databases datamodel. "Preceeded" by the relational model since the flexibility of this makes it hard to work with. Now re-invented in RDF :)
Walrus is a tool for interactively visualizing large directed graphs in three-dimensional space. By employing a fisheye-like distortion, it provides a display that simultaneously shows local detail and the global context.
software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive b
The Boost Graph Library Python bindings (which we refer to as "BGL-Python") expose the functionality of the Boost Graph Library and Parallel Boost Graph Library as a Python package, allowing one to perform computation-intensive tasks on graphs (or network
Paper in which we describe how an artificial chemistry on a planar graph easily generates islands of activity with barriers with much lower activity between them
P. Heim, J. Ziegler, and S. Lohmann. Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW 2008), volume 417 of CEUR Workshop Proceedings, page 49--58. Aachen, (2008)
D. Yang, P. Rosso, B. Li, and P. Cudre-Mauroux. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 1162–1172. New York, NY, USA, Association for Computing Machinery, (2019)
P. Chapman, G. Stapleton, J. Howse, and I. Oliver. 2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), page 87-94. (September 2011)
X. Wang, and M. Zhang. Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, page 23341--23362. PMLR, (17--23 Jul 2022)
J. Feng, Y. Chen, F. Li, A. Sarkar, and M. Zhang. Advances in Neural Information Processing Systems, 35, page 4776--4790. Curran Associates, Inc., (2022)
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger. Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, page 6861--6871. PMLR, (09--15 Jun 2019)
P. Heim, J. Ziegler, and S. Lohmann. Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW 2008), volume 417 of CEUR Workshop Proceedings, page 49--58. Aachen, (2008)
R. Dindokar, N. Choudhury, and Y. Simmhan. IEEE International Workshop on Parallel and Distributed Computing for Large Scale Machine Learning and Big Data Analytics (ParLearning), Co-located with IPDPS, page 1185--1190. (2015)Short paper.
X. Liu, T. Zhu, H. Tan, and R. Zhang. The Semantic Web--ISWC 2022: 21st International Semantic Web Conference, Virtual Event, October 23--27, 2022, Proceedings, page 284--302. Springer, (2022)
H. Nguyen, N. Nguyen, H. Doan, Z. Ahmadi, T. Doan, and L. Jiang. Proceedings of the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, New York, NY, USA, Association for Computing Machinery, (November 2022)