16,100 research outputs found
Prediction of scientific collaborations through multiplex interaction networks
Link prediction algorithms can help to understand the structure and dynamics
of scientific collaborations and the evolution of Science. However, available
algorithms based on similarity between nodes of collaboration networks are
bounded by the limited amount of links present in these networks. In this work,
we reduce the latter intrinsic limitation by generalizing the Adamic-Adar
method to multiplex networks composed by an arbitrary number of layers, that
encode diverse forms of scientific interactions. We show that the new metric
outperforms other single-layered, similarity-based scores and that scientific
credit, represented by citations, and common interests, measured by the usage
of common keywords, can be predictive of new collaborations. Our work paves the
way for a deeper understanding of the dynamics driving scientific
collaborations, and provides a new algorithm for link prediction in multiplex
networks that can be applied to a plethora of systems
Fast Multiplex Graph Association Rules for Link Prediction
Multiplex networks allow us to study a variety of complex systems where nodes
connect to each other in multiple ways, for example friend, family, and
co-worker relations in social networks. Link prediction is the branch of
network analysis allowing us to forecast the future status of a network: which
new connections are the most likely to appear in the future? In multiplex link
prediction we also ask: of which type? Because this last question is
unanswerable with classical link prediction, here we investigate the use of
graph association rules to inform multiplex link prediction. We derive such
rules by identifying all frequent patterns in a network via multiplex graph
mining, and then score each unobserved link's likelihood by finding the
occurrences of each rule in the original network. Association rules add new
abilities to multiplex link prediction: to predict new node arrivals, to
consider higher order structures with four or more nodes, and to be memory
efficient. We improve over previous work by creating a framework that is also
efficient in terms of runtime, which enables an increase in prediction
performance. This increase in efficiency allows us to improve a case study on a
signed multiplex network, showing how graph association rules can provide
valuable insights to extend social balance theory.Comment: arXiv admin note: substantial text overlap with arXiv:2008.0835
Multiplex Graph Association Rules for Link Prediction
Multiplex networks allow us to study a variety of complex systems where nodes
connect to each other in multiple ways, for example friend, family, and
co-worker relations in social networks. Link prediction is the branch of
network analysis allowing us to forecast the future status of a network: which
new connections are the most likely to appear in the future? In multiplex link
prediction we also ask: of which type? Because this last question is
unanswerable with classical link prediction, here we investigate the use of
graph association rules to inform multiplex link prediction. We derive such
rules by identifying all frequent patterns in a network via multiplex graph
mining, and then score each unobserved link's likelihood by finding the
occurrences of each rule in the original network. Association rules add new
abilities to multiplex link prediction: to predict new node arrivals, to
consider higher order structures with four or more nodes, and to be memory
efficient. In our experiments, we show that, exploiting graph association
rules, we are able to achieve a prediction performance close to an ideal
ensemble classifier. Further, we perform a case study on a signed multiplex
network, showing how graph association rules can provide valuable insights to
extend social balance theory.Comment: Accepted for publication in 15th International Conference on Web and
Social Media (ICWSM) 202
Prediction of new scientific collaborations through multiplex networks
The establishment of new collaborations among scientists fertilizes the scientific environment, fostering novel discoveries. Understanding the dynamics driving the development of scientific collaborations is thus crucial to characterize the structure and evolution of science. In this work, we leverage the information included in publication records and reconstruct a categorical multiplex networks to improve the prediction of new scientific collaborations. Specifically, we merge different bibliographic sources to quantify the prediction potential of scientific credit, represented by citations, and common interests, measured by the usage of common keywords. We compare several link prediction algorithms based on different dyadic and triadic interactions among scientists, including a recently proposed metric that fully exploits the multiplex representation of scientific networks. Our work paves the way for a deeper understanding of the dynamics driving scientific collaborations, and validates a new algorithm that can be readily applied to link prediction in systems represented as multiplex networks. © 2021, The Author(s)
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