Semi-supervised Graph Embedding Approach to Dynamic Link Prediction

Hisano, Ryohei

doi:10.1007/978-3-319-73198-8_10

Cited by 39 publications

(24 citation statements)

References 26 publications

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“…Topological structure of the social graph is used in designing a mapping function that maps the existing network to a network consisting of all the links. [22]proposed a semi-supervised model to work on a dynamic link prediction problem. By using previous 't' network snapshots, network structure at time t+1 is predicted.…”

Section: A Traditional Approachmentioning

confidence: 99%

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

Behera

Das

Swetanisha

et al. 2021

Wireless Pers Commun

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The Follower Link Prediction is an emerging application preferred by social networking sites to increase their user network. It helps in finding potential unseen individual and can be used for identifying relationship between nodes in social network. With the rapid growth of many users in social media, which users to follow leads to information overload problems. Previous works on link prediction problem are generally based on local and global features of a graph and limited to a smaller dataset. The number of users in social media is increasing in an extraordinary rate. Generating features for supervised learning from a large user network is challenging. In this paper, a supervised learning model (LPXGB) using XGBoost is proposed to consider the link prediction problem as a binary classification problem. Many hybrid graph feature techniques are used to represent the dataset suitable for machine learning. The efficiency of the LPXGB model is tested with three real world datasets Karate, Polblogs and Facebook. The proposed model is compared with various machine learning classifiers and also with traditional link prediction models. Experimental results are evident that the proposed model achieves higher classification accuracy and AUC value.

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Section: A Traditional Approachmentioning

confidence: 99%

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

Behera

Das

Swetanisha

et al. 2021

Wireless Pers Commun

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“…However, the aforementioned approaches still have limited room for the improvement of prediction accuracy, because they are almost based on the traditional linear model, ignoring the potential non-linear characteristic of the dynamic network. Although several non-linear methods based on the restricted Boltzmann machine (RBM) [18] and graph embedding [19] are proposed, most of them can only be applied to the prediction of unweighted networks but cannot deal with the challenging case of weighted networks.…”

Section: Related Workmentioning

confidence: 99%

GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

Lei

Qin

Bai

et al. 2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

159

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In this paper, we generally formulate the dynamics prediction problem of various network systems (e.g., the prediction of mobility, traffic and topology) as the temporal link prediction task. Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks. The proposed model leverages the benefits of the graph convolutional network (GCN), long short-term memory (LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics, topology structure and evolutionary patterns of weighted dynamic networks can be fully exploited to improve the temporal link prediction performance. Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, which can effectively tackle the sparsity and the wide-value-range problem of edge weights in real-life dynamic networks. To verify the model's effectiveness, we conduct extensive experiments on four datasets of different network systems and application scenarios. The experimental results demonstrate that our model achieves impressive results compared to the state-of-the-art competitors.

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“…Link prediction is an ubiquitous problem in recommendation system [6], social media [9], medicine [18] and even finance [9]. Most of these methods are the discriminative models to classify the unknown links as the existing or the nonexisting links for the target networks [19].…”

Section: Related Workmentioning

confidence: 99%

“…Many real-world applications could be modeled as link prediction problems. For example, the recommendation system could be treated as a network system learns to connect user nodes with product nodes [6]; the recommendation of friends in the social media is the prediction for the future links based on the current social network structure [17]; even the financial risk could be discussed through the link formation probabilities between the financial organizations in an economic network [9]. Two mainstream categories in link prediction are either based on the statistical patterns of the link formation behaviors of the network [10,2,17] or the graph representation learning [31,33] methods which embed nodes as vectors with respect to the network topological information.…”

Section: Introductionmentioning

confidence: 99%

Generative temporal link prediction via self-tokenized sequence modeling

et al. 2020

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We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future links for temporal networks. GLSM captures the temporal link formation patterns from the observed links with a sequence modeling framework and has the ability to generate the emerging links by inferring from the probability distribution on the potential future links. To avoid overfitting caused by treating each link as a unique token, we propose a self-tokenization mechanism to transform each raw link in the network to an abstract aggregation token automatically. The self-tokenization is seamlessly integrated into the sequence modeling framework, which allows the proposed GLSM model to have the generalization capability to discover link formation patterns beyond raw link sequences. We compare GLSM with the existing state-of-art methods on five real-world datasets. The experimental results demonstrate that GLSM obtains future positive links effectively in a generative fashion while achieving the best performance (2-10% improvements on AUC) among other alternatives.

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Semi-supervised Graph Embedding Approach to Dynamic Link Prediction

Cited by 39 publications

References 26 publications

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks

Generative temporal link prediction via self-tokenized sequence modeling

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