Clones in Graphs

Abstract

Finding structural similarities in graph data, like social networks, is a far-ranging task in data mining and knowledge discovery. A (conceptually) simple reduction would be to compute the automorphism group of a graph. However, this approach is ineffective in data mining since real world data does not exhibit enough structural regularity. Here we step in with a novel approach based on mappings that preserve the maximal cliques. For this we exploit the well known correspondence between bipartite graphs and the data structure formal context (G, M, I) from Formal Concept Analysis. From there we utilize the notion of clone items. The investigation of these is still an open problem to which we add new insights with this work. Furthermore, we produce a substantial experimental investigation of real world data. We conclude with demonstrating the generalization of clone items to permutations.

Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields

“…The authors have also developed new knowledge acquisition methods [4,5], in particular in a collaborative setting [29]. As the specific distribution of the data in feature spaces has impact on efficient learnability of knowledge, the group studied this dependency in the realm of graphs [16] and in the realm of metric measure spaces [28]. The goal of this paper is to complements the existing, mostly conceptual work on CIL with some information on -the usage of terms such as information, knowledge, and experience as used in CIL (Section 2), -remarks on scientific disciplines required in CIL research (Section 3), and -a differentiation of CIL from other research areas and structured research programs (Section 4).…”