Link Prediction in Social Networks: the State-of-the-Art

Wang, Peng; Xu, Bao-Wen; Wu, Yurong; Zhou, Xiaoyu

doi:10.48550/arxiv.1411.5118

Cited by 10 publications

(6 citation statements)

References 10 publications

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“…In all of these methods, a score is computed for all edge-candidate dyads and ultimately the top x set of prospective edges with highest weights are kept (where x is some user-defined parameter). Let m and n be a pair of nodes and Γ(m) denote the set of neighbors for a node m. Then, under the following 3 common link prediction methods [27], we can calculate the score of the potential link as Score(m, n).…”

Section: Using the Fitted Attributed Sbm For Link Prediction And Coll...mentioning

confidence: 99%

Stochastic block models with multiple continuous attributes

et al. 2019

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The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference. In this paper, we consider circumstances in which nodes have an associated vector of continuous attributes that are also used to learn the node-to-community assignments and corresponding SBM parameters. While this assumption is not realistic for every application, our model assumes that the attributes associated with the nodes in a network's community can be described by a common multivariate Gaussian model. In this augmented, attributed SBM, the objective is to simultaneously learn the SBM connectivity probabilities with the multivariate Gaussian parameters describing each community. While there are recent examples in the literature that combine connectivity and attribute information to inform community detection, our model is the first augmented stochastic block model to handle multiple continuous attributes. This provides the flexibility in biological data to, for example, augment connectivity information with continuous measurements from multiple experimental modalities. Because the lack of labeled network data often makes community detection results difficult to validate, we highlight the usefulness of our model for two network prediction tasks: link prediction and collaborative filtering. As a result of fitting this attributed stochastic block model, one can predict the attribute vector or connectivity patterns for a new node in the event of the complementary source of information (connectivity or attributes, respectively). We also highlight two biological examples where the attributed stochastic block model provides satisfactory performance in the link prediction and collaborative filtering tasks.

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Section: Using the Fitted Attributed Sbm For Link Prediction And Coll...mentioning

confidence: 99%

Stochastic block models with multiple continuous attributes

et al. 2019

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“…In the link prediction problem, the data is represented as a graph [17,12,30], and the task consists in predicting links to appear in this graph. When the temporal evolution is a key element in the data, a usual approach is to slice data into several time windows T i , then aggregate them as a sequence of graphs G i = (V, E i ) with E i = {(u, v) : ∃(t, u, v) ∈ E, t ∈ T i }.…”

Section: Link Prediction In Graphsmentioning

confidence: 99%

Predicting interactions between individuals with structural and dynamical information

Arnoux,

Tabourier,

Latapy

2018

Preprint

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Capturing both the structural and temporal aspects of interactions is crucial for many real world datasets like contact between individuals. Using the link stream formalism to capture the dynamic of the systems, we tackle the issue of activity prediction in link streams, that is to say predicting the number of links occurring during a given period of time and we present a protocol that takes advantage of the temporal and structural information contained in the link stream. Using a supervised learning method, we are able to model the dynamic of our system to improve the prediction. We investigate the behavior of our algorithm and crucial elements affecting the prediction. By introducing different categories of pair of nodes, we are able to improve the quality as well as increase the diversity of our prediction.

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“…[5] For DOI: 10.1002/adma.202311040 instance, the number of mutual friends (or connecting edges) in social networks can determine intimacy between individuals (or nodes). [6,7] In contrast, in protein networks, interactions rely on the key and lock model. [8,9] If two proteins are connected with many 3-hop connections, they will interact shortly, even if they are not directly connected.…”

Section: Introductionmentioning

confidence: 99%

Cross‐Wired Memristive Crossbar Array for Effective Graph Data Analysis

Jang,

Han,

Shim

et al. 2023

Advanced Materials

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Graphs adequately represent the enormous interconnections among numerous entities in big data, incurring high computational costs in analyzing them with conventional hardware. Physical graph representation (PGR) is an approach that replicates the graph within a physical system, allowing for efficient analysis. This study introduces a cross‐wired crossbar array (cwCBA), uniquely connecting diagonal and non‐diagonal components in a CBA by a cross‐wiring process. The cross‐wired diagonal cells enable cwCBA to achieve precise PGR and dynamic node state control. For this purpose, a cwCBA is fabricated using Pt/Ta2O5/HfO2/TiN (PTHT) memristor with high on/off and self‐rectifying characteristics. The structural and device benefits of PTHT cwCBA for enhanced PGR precision are highlighted, and the practical efficacy is demonstrated for two applications. First, it executes a dynamic path‐finding algorithm, identifying the shortest paths in a dynamic graph. PTHT cwCBA shows a more accurate inferred distance and ≈1/3800 lower processing complexity than the conventional method. Second, it analyzes the protein–protein interaction (PPI) networks containing self‐interacting proteins, which possess intricate characteristics compared to typical graphs. The PPI prediction results exhibit an average of 30.5% and 21.3% improvement in area under the curve and F1‐score, respectively, compared to existing algorithms.

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Link Prediction in Social Networks: the State-of-the-Art

Cited by 10 publications

References 10 publications

Stochastic block models with multiple continuous attributes

Stochastic block models with multiple continuous attributes

Predicting interactions between individuals with structural and dynamical information

Cross‐Wired Memristive Crossbar Array for Effective Graph Data Analysis

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