Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks

Gui, Huan; Sun, Yizhou; Han, Jiawei; Brova, George

doi:10.1145/2661829.2662000

Cited by 32 publications

(23 citation statements)

References 30 publications

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“…The independence assumption is removed in [41,52], they tried to model the correlation between multiple competing or cooperating cascades. Other studies [18,20,21,40,44], use the additional information of the di usion network such as topic of tweets or the community structure to better model the in uence network. Most of the previous works studied the information di usion on microblogging networks like Twitter, whereas we try to model the time and location of users' check-ins in the location-based networks like Foursquare.…”

Section: Prior Workmentioning

confidence: 99%

Recurrent spatio-temporal modeling of check-ins in location-based social networks

2018

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Social networks are getting closer to our real physical world. People share the exact location and time of their check-ins and are influenced by their friends. Modeling the spatio-temporal behavior of users in social networks is of great importance for predicting the future behavior of users, controlling the users’ movements, and finding the latent influence network. It is observed that users have periodic patterns in their movements. Also, they are influenced by the locations that their close friends recently visited. Leveraging these two observations, we propose a probabilistic model based on a doubly stochastic point process with a periodic-decaying kernel for the time of check-ins and a time-varying multinomial distribution for the location of check-ins of users in the location-based social networks. We learn the model parameters by using an efficient EM algorithm, which distributes over the users, and has a linear time complexity. Experiments on synthetic and real data gathered from Foursquare show that the proposed inference algorithm learns the parameters efficiently and our method models the real data better than other alternatives.

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Section: Prior Workmentioning

confidence: 99%

Recurrent spatio-temporal modeling of check-ins in location-based social networks

2018

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“…Link prediction is one of the core techniques of complex network analysis, which has been widely used in many applications [5,13,14]. Related literatures on link prediction can be classified into two categories: unsupervised link prediction [15] and supervised link prediction [16].…”

Section: Related Workmentioning

confidence: 99%

“…In protein-protein interaction networks [3], protein molecular is node, and mining interactions between nodes is helpful to reveal the protein function and determine biological mechanism. Moreover, in bibliographic networks, the heterogeneous type of collaborative relations is a useful tool to detect the scientist community (circle) [4] and model topic diffusion [5].…”

Section: Introductionmentioning

confidence: 99%

Weighted Local Naive Bayes Link Prediction

Wu¹,

Zhang²,

Ren³

et al. 2017

J Inf Process Syst

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Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

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“…Our motivation is to employ a deep learning approach on information diffusion tasks such as topic diffusion and cascade prediction to alleviate the problems of the earlier ones. Most works on topic diffusion suffer from local similarity considerations and single node-based embedding [10], our latent based representational learning approach investigates a global view of the activation prediction modeling. Local similarity computations for each two nodes are time-consuming and unendurable for big networks.…”

Section: Introductionmentioning

confidence: 99%

“…DBLP, PubMed, ACM, APS, and Citeseer. HDD is compared with multiple strong baselines, including feature-based methods such as deepwalk [32], node2vec [33], deepcas [9], and MLTM-R [10]. HDD method significantly improves the results over these baselines.…”

Section: Introductionmentioning

confidence: 99%

Deep learning approach on information diffusion in heterogeneous networks

Molaei

Zare

Veisi

2020

Knowledge-Based Systems

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There are many real-world complex systems with multi-type interacting entities that can be regarded as heterogeneous networks including human connections and biological evolutions. One of the main issues in such networks is to predict information diffusion such as shape, growth and size of social events and evolutions in the future. While there exist a variety of works on this topic mainly using a threshold-based approach, they suffer from the local viewpoint on the network and sensitivity to the threshold parameters. In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model. To this end, we propose a novel meta-path representation learning approach, Heterogeneous Deep Diffusion(HDD), to exploit meta-paths as main entities in networks. At first, the functional heterogeneous structures of the network are learned by a continuous latent representation through traversing meta-paths with the aim of global end-to-end viewpoint. Then, the wellknown deep learning architectures are employed on our generated features to predict diffusion processes in the network. The proposed approach enables us to apply it on different information diffusion tasks such as topic diffusion and cascade prediction. We demonstrate the proposed approach on benchmark network datasets through the well-known evaluation measures. The experimental results show that our approach outperforms the earlier state-of-the-art methods.

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Modeling Topic Diffusion in Multi-Relational Bibliographic Information Networks

Cited by 32 publications

References 30 publications

Recurrent spatio-temporal modeling of check-ins in location-based social networks

Recurrent spatio-temporal modeling of check-ins in location-based social networks

Weighted Local Naive Bayes Link Prediction

Deep learning approach on information diffusion in heterogeneous networks

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