CoupledLP

Abstract

We study the problem of link prediction in coupled networks, where we have the structure information of one (source) network and the interactions between this network and another (target) network. The goal is to predict the missing links in the target network. The problem is extremely challenging as we do not have any information of the target network. Moreover, the source and target networks are usually heterogeneous and have different types of nodes and links. How to utilize the structure information in the source network for predicting links in the target network? How to leverage the heterogeneous interactions between the two networks for the prediction task?We propose a unified framework, CoupledLP, to solve the problem. Given two coupled networks, we first leverage atomic propagation rules to automatically construct implicit links in the target network for addressing the challenge of target network incompleteness, and then propose a Coupled Factor Graph Model to incorporate the meta-paths extracted from the coupled part of the two networks for transferring heterogeneous knowledge. We evaluate the proposed framework on two different genres of datasets: diseasegene (DG) and mobile social networks. In the DG networks, we aim to use the disease network to predict the associations between genes. In the mobile networks, we aim to use the mobile communication network of one mobile operator to infer the network structure of its competitors. On both datasets, the proposed CoupledLP framework outperforms several alternative methods. The proposed problem of coupled link prediction and the corresponding framework demonstrate both the scientific and business applications in biology and social networks.

Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey

“…Node attribute [45,68], Edge attribute [44,68] Feature distribution, Hill numbers [13] Process Precision [24,63], Recall [25], Area Under the Precision-Recall (AUPR) curve [19], Receiver Operating Characteristic (ROC) curves, Area Under the ROC (AUC) [46], Geometric Mean of AUC and PRAUC (GMAUC) [34], Error Rate [15], SumD [41], Kendall's Tau Coefficient (KTC) [9], Micro/Macro/Weighted Average Precision/Recall/F1 Score [14,54] Local 2-sample Kullback-Leibler divergence [18], Manhattan distance [21], Canberra Distance [21], Euclidean distance [39], Matusita distance [39] Global 2-sample Kullback-Leibler divergence [18], Manhattan distance [21], Canberra Distance [21], Euclidean distance [39], Matusita distance [39], Earth Mover's Distance [52], Similarity metrics respectively based on entropy distance, spectral distance, modality distance, cosine of the angle between two graphs [64].…”

On relationship formation in heterogeneous information networks: An inferring method based on multilabel learning

“…There are two kinds of approaches. One focuses on leveraging the estimated parameters in the source network to improve the prediction performance in the target network based on the common features between them, named as transfer link prediction [10,11,31,37,38]. The other aims at predicting links in the cross part of two networks [18,36,38].…”