Understanding and Predicting Delay in Reciprocal Relations

Li, Jundong; Tang, Jiliang; Wang, Yilin; Wan, Yali; Chang, Yi; Liu, Huan

doi:10.1145/3178876.3186076

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…The past decade has witnessed the development of a great number of link prediction methods [7,8,11,13,20,33,37,44,48,54,55]. The vast majority of existing link prediction methods can be broadly categorized into two classes -unsupervised methods and supervised methods.…”

Section: Link Predictionmentioning

confidence: 99%

Streaming Link Prediction on Dynamic Attributed Networks

Cheng

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

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Link prediction targets to predict the future node interactions mainly based on the current network snapshot. It is a key step in understanding the formation and evolution of the underlying networks; and has practical implications in many real-world applications, ranging from friendship recommendation, click through prediction to targeted advertising. Most existing efforts are devoted to plain networks and assume the availability of network structure in memory before link prediction takes place. However, this assumption is untenable as many real-world networks are affiliated with rich node attributes, and often, the network structure and node attributes are both dynamically evolving at an unprecedented rate. Even though recent studies show that node attributes have an added value to network structure for accurate link prediction, it still remains a daunting task to support link prediction in an online fashion on such dynamic attributed networks. As changes in the dynamic attributed networks are often transient and can be endless, link prediction algorithms need to be efficient by making only one pass of the data with limited memory overhead. To tackle these challenges, we study a novel problem of streaming link prediction on dynamic attributed networks and present a novel framework-SLIDE. Methodologically, SLIDE maintains and updates a low-rank sketching matrix to summarize all observed data, and we further leverage the sketching matrix to infer missing links on the fly. The whole procedure is theoretically guaranteed, and empirical experiments on real-world dynamic attributed networks validate the effectiveness and efficiency of the proposed framework.

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Section: Link Predictionmentioning

confidence: 99%

Streaming Link Prediction on Dynamic Attributed Networks

Cheng

et al. 2018

Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Self Cite

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“…Reciprocal relationships are also explored in Bipartite social network of online dating for providing date partner recommendations (Zhao et al , 2014; Xia et al , 2016). Recently, a very interesting issue of understanding and predicting delay in reciprocal relations is investigated (Li et al , 2017). They stated that accurate prediction of delay has a crucial role in the progress of several real world applications, such as, friend recommendation and product or service marketing campaign.…”

Section: Related Workmentioning

confidence: 99%

Who will cite you back? Reciprocal link prediction in citation networks

Daud

Ahmed

Amjad

et al. 2017

LHT

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Purpose Link prediction in social networks refers toward inferring the new interactions among the users in near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in citations networks refers toward inferring about getting a citation from an author, whose work is already cited by you. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors study the extent to which the information of a two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features based on papers, their authors and citations of each paper to predict reciprocal links. Findings Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst reciprocal links. Research limitations/implications It is not a generic method for link prediction which can work for different networks with relevant features and parameters. Practical implications This paper predicts the reciprocal links to show who is citing your work to collaborate with them in future. Social implications The proposed method will be helpful in finding collaborators and developing academic links. Originality/value The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.

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“…With the development of IoT, more and more domains, e.g., social media and industries, have accumulated a large amount of high-dimensional data with temporal orders, socalled multivariate time series (MTS), which contain valuable information. MTS data containing a large number of features become more and more common in various applications, such as in biology [1], multimedia [2], social networks [3], energy [4], and industries [5,6]. It has brought the curse of dimensionality and volume.…”

Section: Introductionmentioning

confidence: 99%

A Multiattention‐Based Supervised Feature Selection Method for Multivariate Time Series

Cao

Chen

Zhang

et al. 2021

Computational Intelligence and Neuroscience

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Feature selection is a known technique to preprocess the data before performing any data mining task. In multivariate time series (MTS) prediction, feature selection needs to find both the most related variables and their corresponding delays. Both aspects, to a certain extent, represent essential characteristics of system dynamics. However, the variable and delay selection for MTS is a challenging task when the system is nonlinear and noisy. In this paper, a multiattention-based supervised feature selection method is proposed. It translates the feature weight generation problem into a bidirectional attention generation problem with two parallel placed attention modules. The input 2D data are sliced into 1D data from two orthogonal directions, and each attention module generates attention weights from their respective dimensions. To facilitate the feature selection from the global perspective, we proposed a global weight generation method that calculates a dot product operation on the weight values of the two dimensions. To avoid the disturbance of attention weights due to noise and duplicated features, the final feature weight matrix is calculated based on the statistics of the entire training set. Experimental results show that this proposed method achieves the best performance on compared synthesized, small, medium, and practical industrial datasets, compared to several state-of-the-art baseline feature selection methods.

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Understanding and Predicting Delay in Reciprocal Relations

Cited by 6 publications

References 41 publications

Streaming Link Prediction on Dynamic Attributed Networks

Streaming Link Prediction on Dynamic Attributed Networks

Who will cite you back? Reciprocal link prediction in citation networks

A Multiattention‐Based Supervised Feature Selection Method for Multivariate Time Series

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