Predictability of missing links in complex networks

García-Pérez, Guillermo; Aliakbarisani, Roya; Ghasemi, Abdorasoul; Serrano, M. Ángeles

doi:10.48550/arxiv.1902.00035

Cited by 3 publications

(7 citation statements)

References 25 publications

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“…Our results also resolve the controversy among earlier reports on link prediction using hyperbolic geometry [23,[32][33][34][35][36]. These reports approached link prediction using different measures of link prediction accuracy.…”

Section: Discussionsupporting

confidence: 83%

“…This effect is consistent with a recent observation in Ref. [23] that the upper bound of link predictability in edge-independent graphs increases with 1 − q. On the other hand, as 1−q increases, the accuracy of RA de- 6: Link prediction accuracy for HRGs with inferred coordinates: AUPR.…”

Section: A Tests On Hrg Graphs With Inferred Coordinatessupporting

confidence: 91%

“…Given the combination of these attractive properties, one could naturally expect that the hyperbolic latent space model must be a powerful tool in link prediction. Yet the previous studies on this subject reported mixed results [23,[32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 94%

“…In doing so we pursue several goals. The HRGs provide the upper bound for link prediction accuracy of the HyperLink if same node coordinates are used both for the construction of the graphs and for link prediction [23]. Thus, we want to quantify the upper bound of link predictability on HRG graphs.…”

Section: Link Prediction With Known Coordinatesmentioning

confidence: 99%

“…Latent distances model similarity between nodes, and the main idea behind these models is to model homophily: more similar nodes are more likely to be linked. Link prediction then reduces to ranking unconnected node pairs in the order of increasing latent distances between them: the closer the two unlinked nodes in the latent space, the higher the probability of a missing link [4,[21][22][23].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Link prediction with hyperbolic geometry

Kitsak,

Voitalov,

Krioukov

2019

Preprint

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Section: Discussionsupporting

confidence: 83%

Section: A Tests On Hrg Graphs With Inferred Coordinatessupporting

confidence: 91%

Section: Introductionmentioning

confidence: 94%

Section: Link Prediction With Known Coordinatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Link prediction with hyperbolic geometry

Kitsak,

Voitalov,

Krioukov

2019

Preprint

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Latent geometry of bipartite networks

2017

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Despite the abundance of bipartite networked systems, their organizing principles are less studied, compared to unipartite networks. Bipartite networks are often analyzed after projecting them onto one of the two sets of nodes. As a result of the projection, nodes of the same set are linked together if they have at least one neighbor in common in the bipartite network. Even though these projections allow one to study bipartite networks using tools developed for unipartite networks, one-mode projections lead to significant loss of information and artificial inflation of the projected network with fully connected subgraphs. Here we pursue a different approach for analyzing bipartite systems that is based on the observation that such systems have a latent metric structure: network nodes are points in a latent metric space, while connections are more likely to form between nodes separated by shorter distances. This approach has been developed for unipartite networks, and relatively little is known about its applicability to bipartite systems. Here, we fully analyze a simple latent-geometric model of bipartite networks, and show that this model explains the peculiar structural properties of many real bipartite systems, including the distributions of common neighbors and bipartite clustering. We also analyze the geometric information loss in one-mode projections in this model, and propose an efficient method to infer the latent pairwise distances between nodes. Uncovering the latent geometry underlying real bipartite networks can find applications in diverse domains, ranging from constructing efficient recommender systems to understanding cell metabolism.

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A Link Prediction Algorithm Based on Weighted Local and Global Closeness

Wang,

Ning,

Nie

et al. 2023

Entropy

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Link prediction aims to identify unknown or missing connections in a network. The methods based on network structure similarity, known for their simplicity and effectiveness, have garnered widespread attention. A core metric in these methods is “proximity”, which measures the similarity or linking probability between two nodes. These methods generally operate under the assumption that node pairs with higher proximity are more likely to form new connections. However, the accuracy of existing node proximity-based link prediction algorithms requires improvement. To address this, this paper introduces a Link Prediction Algorithm Based on Weighted Local and Global Closeness (LGC). This algorithm integrates the clustering coefficient to enhance prediction accuracy. A significant advantage of LGC is its dual consideration of a network’s local and global features, allowing for a more precise assessment of node similarity. In experiments conducted on ten real-world datasets, the proposed LGC algorithm outperformed eight traditional link prediction methods, showing notable improvements in key evaluation metrics, namely precision and AUC.

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Predictability of missing links in complex networks

Cited by 3 publications

References 25 publications

Link prediction with hyperbolic geometry

Link prediction with hyperbolic geometry

Latent geometry of bipartite networks

A Link Prediction Algorithm Based on Weighted Local and Global Closeness

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