Multiplex flows in citation networks

Abstract

Knowledge is created and transmitted through generations, and innovation is often seen as a process generated from collective intelligence. There is rising interest in studying how innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherits. A citation network can be seen as a perfect example of one generative process leading to innovation. However, the impact and influence of scientific publication are always difficult to capture and measure. We offer a new take on investigating how the knowledge circulates and is transmitted, inspired by the notion of “stream of knowledge”. We propose to look at this question under the lens of flows in directed acyclic graphs (DAGs). In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. From this point on, we can take a finer look at the diffusion of knowledge through the lens of a multiplex network. In this network, each citation of a specific work constitutes one layer of interaction. Within our framework, we design three measures of multiplex flows in DAGs, namely the aggregated, sum and selective flow, to better understand how citations are influenced. We conduct our experiments with the arXiv HEP-Th dataset, and find insights through the visualization of these multiplex networks.

MuzLink: Connected beeswarm timelines for visual analysis of musical adaptations and artist relationships

“…Yet, higher-order questions such as “How an artist’s popularity, in terms of adaptations, impacted the number of its adaptations over time?” are difficult to answer with the proposed approach. A future research avenue could be to derive some artist popularity metrics that could added to the network structure to enable the analysis of flows of influence, like the approach developed by Renoust et al 61 for citation networks, using h -index as a scientist metric. More generally, the goal is to understand how the different layers of the graph are entangled.…”

Introducing Multilayer Stream Graphs and Layer Centralities

“…Considering the changes of topologies in an overall graph is however relevant for the study of spreading processes [7,11], which demonstrated the dependency on spreading from layers coupling. These works also point out how the evolution of centrality is key to studying the network [24], and isolating nodes of interest, such as in citation networks [21].…”

“…This is useful, for example, to inspect homophily within groups of documents [23]. However, multilayer networks show some limitations in fully capturing interactions that exist over time [21], beyond the dynamic of a graph as a whole. To cope with many individual time-dependant interactions, stream graphs [16] offer a comprehensive formalism to deal with real-world sequences of interactions over time.…”

Measure the Impact of Institution and Paper Via Institution-Citation Network

“…S CIENTIFIC impact is evaluated at different levels, ranging from high level at national and institutional scales to low level at researcher and paper scales [1]- [3]. Many studies focus on scientific impact measure, scholarly network analysis, and success of science [4]- [8]. While many of these studies explore scientific impact at a particular timeframe, there's a growing interest in understanding the evolution of scientific impact in "science of science" [9], [10].…”