Structural Dynamics of Knowledge Networks

Abstract

We investigate the structural patterns of the appearance and disappearance of links in dynamic knowledge networks. Human knowledge is nowadays increasingly created and curated online, in a collaborative and highly dynamic fashion. The knowledge thus created is interlinked in nature, and an important open task is to understand its temporal evolution. In this paper, we study the underlying mechanisms of changes in knowledge networks which are of structural nature, i.e., which are a direct result of a knowledge network's structure. Concretely, we ask whether the appearance and disappearance of interconnections between concepts (items of a knowledge base) can be predicted using information about the network formed by these interconnections. In contrast to related work on this problem, we take into account the disappearance of links in our study, to account for the fact that the evolution of collaborative knowledge bases includes a high proportion of removals and reverts. We perform an empirical study on the best-known and largest collaborative knowledge base, Wikipedia, and show that traditional indicators of structural change used in the link analysis literature can be classified into four classes, which we show to indicate growth, decay, stability and instability of links. We finally use these methods to identify the underlying reasons for individual additions and removals of knowledge links.

DYMOND: DYnamic MOtif-NoDes Network Generative Model

“…We study the effectiveness of this approach on the Enron Emails and EU Emails datasets, described in Subsubsections 5.2.1 and 5.2.2 respectively. Additionally, we use a Wikipedia Links dataset, which shows the evolution of hyperlinks between Wikipedia articles [11,25]. The nodes represent articles.…”

WikiLinkGraphs: A Complete, Longitudinal and Multi-Language Dataset of the Wikipedia Link Networks

“…The problem of predicting the appearance of links in time-evolving networks has received significant attention (Lü and Zhou 2011); the problem of predicting their disappearance, on the other hand, is less studied. Preusse and collaborators (Preusse et al 2013) investigated the structural patterns of the evolution of links in dynamic knowledge networks. To do so, they adapt some indicators from sociology and identify four classes to indicate growth, decay, stability and instability of links.…”