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Of all publications in the section: 2
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Article
Batagelj V., PRAPROTNIK S. Social Network Analysis and Mining. 2016. Vol. 6. No. 1. P. 1-22.

n a temporal network, the presence and activity of nodes and links can change through time. To describe temporal networks we introduce the notion of temporal quantities. We define the addition and multiplication of temporal quantities in a way that can be used for the definition of addition and multiplication of temporal networks. The corresponding algebraic structures are semirings. The usual approach to (data) analysis of temporal networks is to transform the network into a sequence of time slices—static networks corresponding to selected time intervals and analyze each of them using standard methods to produce a sequence of results. The approach proposed in this paper enables us to compute these results directly. We developed fast algorithms for the proposed operations. They are available as an open source Python library TQ (Temporal Quantities) and a program Ianus. The proposed approach enables us to treat as temporal quantities also other network characteristics such as degrees, connectivity components, centrality measures, Pathfinder skeleton, etc. To illustrate the developed tools we present some results from the analysis of Franzosi’s violence network and Corman’s Reuters terror news network.

Added: Nov 1, 2018
Article
Shalileh S., Mirkin B. Social Network Analysis and Mining. 2021. Vol. 11. No. 1. P. 1-23.

A feature-rich network is a network whose nodes are characterized by categorical or quantitative features. We propose a data-driven model for finding a partition of the nodes to approximate both the network link data and the feature data. The model involves summary quantitative characteristics of both network links and features. We distinguish between two modes of using the network link data. One mode postulates that the link values are comparable and summable across the network (summability); the other assumption models the case in which different nodes represent different measurement systems so that the link data are neither comparable, nor summable, across different nodes (nonsummability). We derive a Pythagorean decomposition of the combined data scatter involving our data recovery least-squares criterion. We address an equivalent problem of maximizing its complementary part, the contribution of a found partition to the combined data scatter. We follow a doubly greedy strategy in maximizing that. First, communities are found one-by-one, and second, entities are added one-by-one in the process of identifying a community. Our algorithms determine the number of clusters automatically. The nonsummability version proves to have a niche of its own; also, it is faster than the other version. In our experiments, they appear to be competitive over generated synthetic data sets and six real-world data sets from the literature.

Added: Jul 29, 2021