Link Prediction In Heterogeneous Networks Research Articles

Resisting the Edge-Type Disturbance for Link Prediction in Heterogeneous Networks

The rapid development of heterogeneous networks has proposed new challenges to the long-standing link prediction problem. Existing models trained on the verified edge samples from different types usually learn type-specific knowledge, and their type-specific predictions may be contradictory for unverified edge samples with uncertain types. This challenge is termed edge-type disturbance in link prediction in heterogeneous networks. To address this challenge, we develop a disturbance-resilient prediction method ( DRPM ) comprising a structural characterizer, a type differentiator, and a resilient predictor. The structural characterizer is responsible for learning edge representations for link prediction. Concurrently, the type differentiator distinguishes type-specific edge representations to generate diverse type experts while maximizing their link prediction performances on specific types. Furthermore, the resilient predictor evaluates the reliability weights of different type experts to develop a resilient prediction mechanism to aggregate discriminable predictions. Extensive experiments conducted on various real-world datasets demonstrate the importance of the explainable introduction of the edge-type disturbance and the superiority of DRPM over state-of-the-art methods.

Link prediction in heterogeneous networks based on metapath projection and aggregation

Link prediction in heterogeneous networks based on metapath projection and aggregation

Type-Aware Anchor Link Prediction across Heterogeneous Networks Based on Graph Attention Network

Anchor Link Prediction (ALP) across heterogeneous networks plays a pivotal role in inter-network applications. The difficulty of anchor link prediction in heterogeneous networks lies in how to consider the factors affecting nodes alignment comprehensively. In recent years, predicting anchor links based on network embedding has become the main trend. For heterogeneous networks, previous anchor link prediction methods first integrate various types of nodes associated with a user node to obtain a fusion embedding vector from global perspective, and then predict anchor links based on the similarity between fusion vectors corresponding with different user nodes. However, the fusion vector ignores effects of the local type information on user nodes alignment. To address the challenge, we propose a novel type-aware anchor link prediction across heterogeneous networks (TALP), which models the effect of type information and fusion information on user nodes alignment from local and global perspective simultaneously. TALP can solve the network embedding and type-aware alignment under a unified optimization framework based on a two-layer graph attention architecture. Through extensive experiments on real heterogeneous network datasets, we demonstrate that TALP significantly outperforms the state-of-the-art methods.

Collaborative linear manifold learning for link prediction in heterogeneous networks

Link prediction in heterogeneous networks aims at predicting missing interactions between pairs of nodes with the help of the topology of the target network and interconnected auxiliary networks. It has attracted considerable attentions from both computer science and bioinformatics communities in the recent years. In this paper, we introduce a novel Collaborative Linear Manifold Learning (CLML) algorithm. It can optimize the consistency of nodes similarities by collaboratively using the manifolds embedded between the target network and the auxiliary network. The experiments on four benchmark datasets have demonstrated the outstanding advantages of CLML, not only in the high prediction performance compared to baseline methods, but also in the capability to predict the unknown interactions in the target networks accurately and effectively.

A new classification algorithm recommendation method based on link prediction

Recommending appropriate classification algorithm(s) for a given classification problem is of great significance and also one of the challenging problems in the field of data mining, which is usually viewed as a meta-learning problem. Multi-label learning has been adopted and validated to be an effective meta-learning method in classification algorithm recommendation. However, the multi-label learning method used in previous classification algorithm recommendation relies only on relationship between data sets and their direct neighbours, ignoring the impact of other data sets. In this paper, a new classification algorithm recommendation method based on link prediction between data sets and classification algorithms is proposed. Taking advantage of link prediction in heterogeneous networks, this method considers the impact of all data sets and makes full use of the interactions between data sets as well as between data sets and algorithms. Firstly, meta data of the training data sets is collected. And then a heterogeneous network called DAR (Data and Algorithm Relationship) Network is constructed with the meta data. Finally, the link prediction technique is adopted to recommend appropriate algorithm(s) for a given data set on the basis of the DAR Network. To evaluate the proposed link prediction-based recommendation method, extensive experiments with 131 data sets and 21 classification algorithms are conducted. Results of 5 performance measures indicate that the proposed method is more effective compared with the base line classification algorithm recommendation method and can be used in practice.

Link Prediction in Heterogeneous Networks Based on Tensor Factorization

Link Prediction, that is, predicting the formation of links or interactions in a network in the future, is an impor- tant task in network analysis. Link prediction provides useful insights for other applications, such as recommendation sys- tem, disease-gene candidate detection and so on. Most link prediction methods assume that there is only one single type in the network. However, many real-world networks have heterogeneous interactions. Link prediction in such networks is challenging since (a) the network has a complicated dependency structure; and (b) the links of different types may carry different kinds of semantic meanings, which is important to distinguish the formation mechanisms of each link type. In this paper, we address these challenges by proposing a general method based on tensor factorization for link prediction in heterogeneous networks. Using a CANDECOMP/PARAFAC tensor factorization of the data, we illustrate the usefulness of exploring the natural three-dimensional structure of heterogeneous network. The experiment on real-world heterogene- ous network demonstrates the effectiveness and efficiency of our methodology.

A framework for dynamic link prediction in heterogeneous networks

AbstractNetwork and linked data have become quite prevalent in recent years because of the ubiquity of the web and social media applications, which are inherently network oriented. Such networks are massive, dynamic, contain a lot of content, and may evolve over time. In this paper, we will study the problem of efficient dynamic link inference in temporal and heterogeneous information networks. The problem of efficiently performing dynamic link inference is extremely challenging in massive and heterogeneous information network because of the challenges associated with the dynamic nature of the network, and the different types of nodes and attributes in it. Both the topology and type information need to be used effectively for the link inference process. We propose an effective two‐level scheme which makes efficient macro‐ and micro‐decisions for combining structure and content in a dynamic and time‐sensitive way. The time‐sensitive nature of the links is leveraged in order to perform effective link prediction. We will also study how to apply the method to the problem of community prediction. We illustrate the effectiveness of our technique over a number of real data sets. Statistical Analysis and Data Mining 2013 DOI: 10.1002/sam.11198