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Showing 1 to 20 of 51 for “"Link Prediction"”.
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Link Prediction on Distributed Systems
… (GNNs) and transformer-based architectures, for link prediction tasks. Despite their success, these models struggle with large-scale, temporal data and limited generalization capabilities. This research addresses these challenges by developing a diffusion-based model that incorporates advanced …
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Improving target acquisition in Web applications with link prediction
… users spend considerable time clicking on hyperlinks and buttons to complete frequent tasks. Individual application developers can optimize their interfaces to improve typical usage; however, no single task model will accurately reflect the needs of a wide audience of users. This thesis …
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Neurosymbolic Reasoning for Link Prediction in Supply Chain Knowledge Graphs
This thesis is motivated by recent developments in Supply Chain Management (SCM) and Artificial Intelligence (AI). On one side, as modern supply chains become complex and interconnected with invisible dependencies, we increasingly see disruptions emerging and propagating across the network. This …
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Graph Neural Networks for City Policy Recommendations as a Link Prediction Task
… the policy recommendation task as a GNN link prediction problem, demonstrating its potential to scale data-driven urban governance.
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Learning on the Graph: Link Prediction, Multi-label Learning, and Applications to Integrative Complex Disease Studies
… incompleteness.</p><p>We first investigate the link prediction problem underlying many important applications in the studies of biological networks. We propose a novel link prediction algorithm, Marginalized Denoising Model (MDM), which explicitly acknowledges the data incompleteness. MDM casts …
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Statistical inference in complex networks: community detection, change-point detection, link prediction, and two-sample testing
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-08-01
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Link prediction and link detection in sequences of large social networks using temporal and local metrics
… introduced by Liben-Nowell and Kleinberg in The Link Prediction Problem for Social Networks [42]. Link prediction is the problem of predicting between which unconnected nodes in a graph a link will form next, based on the current structure of the graph.
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Improving Automated Literature-based Discovery with Neural Networks: Neural biomedical Named Entity Recognition, Link Prediction and Discovery
… random- and time-sliced biomedical graphs using link prediction and 3) improving the ranking of published discoveries on open- and closed- LBD instances by scoring the strength of connection paths using neural models. Excitingly, the latter approaches outperformed those used by the …
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Jungčių prognozavimas, paremtas orientuoto tinklo klasterizacijos koeficientu /
In this paper we analyse network link prediction methods based on a concept of digraph clustering coefficient proposed by M. Bloznelis and L. Leskelä. The goal of this research is to define new link prediction indices derived from the clustering coefficient mentioned above. We also aim to …
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Tie Inducement using Closure Analysis in Information Networks
… problem in Social Networks Analysis, namely link prediction. Link Prediction is important to understand and evaluate the change in structure for a certain social network over a given period of time. While different methods exist to address link prediction in this work we explore one such …
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Scalable subgraph representation learning through simplification
Link prediction on graphs is a fundamental problem. Subgraph representation learning approaches (SGRLs), by transforming link prediction to graph classification on the subgraphs around the links, have achieved state-of-the-art performance in link prediction. However, SGRLs are computationally …
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LDA based approach for predicting friendship links in live journal social network
… data mining problems including friendship link and interest predictions, tag recommendations among others. In this work, we consider the friendship link prediction problem and study a topic modeling approach to this problem. Topic models are among the most effective approaches to latent …
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Modelling and prediction of the evolution of fish farm networks under aquatic disease spread
… industries. These transfers form a network linking entities such as farms, hatcheries, and suppliers. When consignments carrying pathogens enter this densely connected system, disease can spread rapidly, disrupting trade flows and creating significant logistical problems. If suppliers cannot …
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Development and evaluation of machine learning algorithms for biomedical applications
Gene network inference and drug response prediction are two important problems in computational biomedicine. The former helps scientists better understand the functional elements and regulatory circuits of cells. The latter helps a physician gain full understanding of the effective treatment on …
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Prediction and modelling of complex social networks and their evolution.
… context of computational approaches for their prediction and modelling. The increasing popularity and advancement of social net- works paired with the availability of social network data enable empirical analysis, inference, prediction and modelling of social patterns. This data-driven approach …
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Social Network Analysis using Cultural Algorithms and its Variants
… field which are called community detection and link prediction. Moreover, a problem of population adaptation through knowledge migration in real-life social systems has been identified to model and study through the proposed method. To the best of our knowledge, this is the first work in the field …
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Automatic Question Answering and Knowledge Discovery from Electronic Health Records
… in tabular EHR, which can be formulated as a link prediction problem in graph domain. We develop a self-supervised learning framework for better representation learning of entities across a large corpus and also consider local contextual information for the down-stream link prediction task. We …
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MACHINE LEARNING FOR TEMPORAL HETEROGENEOUS GRAPHS: PREDICTIVE METHODS, INTERPRETABILITY AND APPLICATIONS.
… tools from temporal network analysis, such as link prediction heuristics, and we systematically benchmark explainability techniques in evolving relational contexts. Finally, we contribute novel high-resolution datasets derived from Web3 social platforms, enabling several applications for …
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Approximate likelihood for dependent networks and hyperlink predictions
… In the second part, we focus on the joint prediction of pairwise link and hyperlink under multi-layer networks to incorporate high-order relations in network, which are not considered in the traditional graph representation models which only predict two-way pairwise relations. We propose a …
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Methods for Constructing and Exploiting Information Measures for Neural Networks
… spotlight. We present our paper on regularising link prediction for graphs with potentially simple generating mechanisms. Specifically, we detail a regularised loss function for link prediction with graph neural networks utilising machinery which claims to estimate the Kolmogorov complexity of …
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