International Network for Social Network Analysis
An organization focused on social networks (no surprise there) but also the source of a number of interesting resources, such as software and data sets.
There is a workshop on NetworkX to be offered at Sunbelt 2011.
Registration for the workshop closes 24 January 2011.
The family tree topic map demonstrated by Eric Freese, years ago now, is one example of a social network.
Both the site and organization merit a close look.
From the website:
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
….
Features:
- Standard graph-theoretic and statistical physics functions
- Easy exchange of network algorithms between applications,
disciplines, and platforms- Many classic graphs and synthetic networks
- Nodes and edges can be "anything"
(e.g. time-series, text, images, XML records)- Exploits existing code from high-quality legacy software in C,
C++, Fortran, etc.- Open source (encourages community input)
- Unit-tested
NetworkX is a nice way to display topic maps as graphs.
Its importance for topic maps lies in the ability to study properties of nodes (representatives of subjects, including relationships) and composition of nodes (merging in topic map speak).
How Complexity Leads to Simplicity.
Ed Berlow in a little over 3 minutes demonstrates the use of an ordered network to discern simplicity in complex graphs.
Any number of factors that contribute to the identification of a subject.
Ordered networks to analyze those factors so we can isolate those that are:
What factors we choose will depend upon our goals and requirements.
Ordered networks may help us make those choices.
Questions:
*I would suggest Graphviz as graph software. You can check under resources for visual editors. You can use other software if you like.
I will walk through creation of a smallish ordered network with Graphviz.
Satrap: Data and Network Heterogeneity Aware P2P Data-Mining Authors: Hock Hee Ang, Vivekanand Gopalkrishnan, Anwitaman Datta, Wee Keong Ng, Steven C. H. Hoi Keywords: Distributed classification, P2P network, cascade SVM
Abstract:
Distributed classification aims to build an accurate classifier by learning from distributed data while reducing computation and communication cost. A P2P network where numerous users come together to share resources like data content, bandwidth, storage space and CPU resources is an excellent platform for distributed classification. However, two important aspects of the learning environment have often been overlooked by other works, viz., 1) location of the peers which results in variable communication cost and 2) heterogeneity of the peers’ data which can help reduce redundant communication. In this paper, we examine the properties of network and data heterogeneity and propose a simple yet efficient P2P classification approach that minimizes expensive inter-region communication while achieving good generalization performance. Experimental results demonstrate the feasibility and effectiveness of the proposed solution.
Among the other claims for Satrap:
- achieves the best accuracy-to-communication cost ratio given that data exchange is performed to improve global accuracy.
- allows users to control the trade-off between accuracy and communication cost with the user-specified parameters.
I find these two the most interesting.
In part because semantic integration, whether explicit or not, is always a question of cost ratio and tradeoffs.
It would be refreshing to see papers that say what semantic integration would be too costly with method X or that aren’t possible with method Y.
Evolutionary Clustering and Analysis of Heterogeneous Information Networks Authors: Manish Gupta; Charu Aggarwal; Jiawei Han; Yizhou Sun Keywords: ENetClus, evolutionary clustering, typed-clustering, DBLP, bibliographic networks
Abstract:
In this paper, we study the problem of evolutionary clustering of multi-typed objects in a heterogeneous bibliographic network. The traditional methods of homogeneous clustering methods do not result in a good typed-clustering. The design of heterogeneous methods for clustering can help us better understand the evolution of each of the types apart from the evolution of the network as a whole. In fact, the problem of clustering and evolution diagnosis are closely related because of the ability of the clustering process to summarize the network and provide insights into the changes in the objects over time. We present such a tightly integrated method for clustering and evolution diagnosis of heterogeneous bibliographic information networks. We present an algorithm, ENetClus, which performs such an agglomerative evolutionary clustering which is able to show variations in the clusters over time with a temporal smoothness approach. Previous work on clustering networks is either based on homogeneous graphs with evolution, or it does not account for evolution in the process of clustering heterogeneous networks. This paper provides the first framework for evolution-sensitive clustering and diagnosis of heterogeneous information networks. The ENetClus algorithm generates consistent typed-clusterings across time, which can be used for further evolution diagnosis and insights. The framework of the algorithm is specifically designed in order to facilitate insights about the evolution process. We use this technique in order to provide novel insights about bibliographic information networks.
Exploring heterogeneous information networks is a first step towards discovery/recognition of new subjects. What other novel insights will emerge from work on heterogeneous information networks only future research can answer.
From Moby-Dick To Mashups: Thinking About Bibliographic Networks was reported by the The FRBR Blog with the following summary:
Summary: Traditional and contemporary attempts to identify and describe simple and complex bibliographic resources have overlooked useful and powerful possibilities, due to the insufficient modeling of “bibliographic things of interest.” The presentation will introduce a resource description approach that remodels and strengthens FRBR by borrowing key concepts from Information Science and the History of Science. The presentation will reveal portions of a network of bibliographic (and other useful) relationships between printings of Melville?s novel dating from 1851-1975 into the present. In addition, structural similarities between the print publication network and the multimedia “mash-ups” seen on YouTube and other websites will be demonstrated and discussed.
Anyone creating a topic map for library resources needs to review these slides.
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