skip to main content


Title: CINES: Explore Citation Network and Event Sequences for Citation Forecasting
Citations of scientific papers and patents reveal the knowledge flow and usually serve as the metric for evaluating their novelty and impacts in the field. Citation Forecasting thus has various applications in the real world. Existing works on citation forecasting typically exploit the sequential properties of citation events, without exploring the citation network. In this paper, we propose to explore both the citation network and the related citation event sequences which provide valuable information for future citation forecasting. We propose a novel Citation Network and Event Sequence (CINES) Model to encode signals in the citation network and related citation event sequences into various types of embeddings for decoding to the arrivals of future citations. Moreover, we propose a temporal network attention and three alternative designs of bidirectional feature propagation to aggregate the retrospective and prospective aspects of publications in the citation network, coupled with the citation event sequence embeddings learned by a two-level attention mechanism for the citation forecasting. We evaluate our models and baselines on both a U.S. patent dataset and a DBLP dataset. Experimental results show that our models outperform the state-of-the-art methods, i.e., RMTPP, CYAN-RNN, Intensity-RNN, and PC-RNN, reducing the forecasting error by 37.76% - 75.32%.  more » « less
Award ID(s):
1717084
NSF-PAR ID:
10303744
Author(s) / Creator(s):
 ;  ;  ;  
Date Published:
Journal Name:
Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. null (Ed.)
    Accurate prediction of scientific impact is important for scientists, academic recommender systems, and granting organizations alike. Existing approaches rely on many years of leading citation values to predict a scientific paper’s citations (a proxy for impact), even though most papers make their largest contributions in the first few years after they are published. In this paper, we tackle a new problem: predicting a new paper’s citation time series from the date of publication (i.e., without leading values). We propose HINTS, a novel end-to-end deep learning framework that converts citation signals from dynamic heterogeneous information networks (DHIN) into citation time series. HINTS imputes pseudo-leading values for a paper in the years before it is published from DHIN embeddings, and then transforms these embeddings into the parameters of a formal model that can predict citation counts immediately after publication. Empirical analysis on two real-world datasets from Computer Science and Physics show that HINTS is competitive with baseline citation prediction models. While we focus on citations, our approach generalizes to other “cold start” time series prediction tasks where relational data is available and accurate prediction in early timestamps is crucial. 
    more » « less
  2. Proc. of 2023 IEEE 39th International Conference on Data Engineering (Ed.)
    Numerous papers get published all the time. However, some papers are born to be well-cited while others are not. In this work, we revisit the important problem of citation prediction, by focusing on the important yet realistic prediction on the average number of citations a paper will attract per year. The task is nonetheless challenging because many correlated factors underlie the potential impact of a paper, such as the prestige of its authors, the authority of its publishing venue, and the significance of the problems/techniques/applications it studies. To jointly model these factors, we propose to construct a heterogeneous publication network of nodes including papers, authors, venues, and terms. Moreover, we devise a novel heterogeneous graph neural network (HGN) to jointly embed all types of nodes and links, towards the modeling of research impact and its propagation. Beyond graph heterogeneity, we find it also important to consider the latent research domains, because the same nodes can have different impacts within different communities. Therefore, we further devise a novel cluster-aware (CA) module, which models all nodes and their interactions under the proper contexts of research domains. Finally, to exploit the information-rich texts associated with papers, we devise a novel text-enhancing (TE) module for automatic quality term mining. With the real-world publication data of DBLP, we construct three different networks and conduct comprehensive experiments to evaluate our proposed CATE-HGN framework, against various state-of-the-art models. Rich quantitative results and qualitative case studies demonstrate the superiority of CATEHGN in citation prediction on publication networks, and indicate its general advantages in various relevant downstream tasks on text-rich heterogeneous networks. 
    more » « less
  3. Abstract Commonly used data citation practices rely on unverifiable retrieval methods which are susceptible to content drift, which occurs when the data associated with an identifier have been allowed to change. Based on our earlier work on reliable dataset identifiers, we propose signed citations, i.e., customary data citations extended to also include a standards-based, verifiable, unique, and fixed-length digital content signature. We show that content signatures enable independent verification of the cited content and can improve the persistence of the citation. Because content signatures are location- and storage-medium-agnostic, cited data can be copied to new locations to ensure their persistence across current and future storage media and data networks. As a result, content signatures can be leveraged to help scalably store, locate, access, and independently verify content across new and existing data infrastructures. Content signatures can also be embedded inside content to create robust, distributed knowledge graphs that can be cited using a single signed citation. We describe applications of signed citations to solve real-world data collection, identification, and citation challenges. 
    more » « less
  4. Large quantities of asynchronous event sequence data such as crime records, emergence call logs, and financial transactions are becoming increasingly available from various fields. These event sequences often exhibit both long-term and short-term temporal dependencies. Variations of neural network based temporal point processes have been widely used for modeling such asynchronous event sequences. However, many current architectures including attention based point processes struggle with long event sequences due to computational inefficiency. To tackle the challenge, we propose an efficient sparse transformer Hawkes process (STHP), which has two components. For the first component, a transformer with a novel temporal sparse self-attention mechanism is applied to event sequences with arbitrary intervals, mainly focusing on short-term dependencies. For the second component, a transformer is applied to the time series of aggregated event counts, primarily targeting the extraction of long-term periodic dependencies. Both components complement each other and are fused together to model the conditional intensity function of a point process for future event forecasting. Experiments on real-world datasets show that the proposed STHP outperforms baselines and achieves significant improvement in computational efficiency without sacrificing prediction performance for long sequences. 
    more » « less
  5. Large quantities of asynchronous event sequence data such as crime records, emergence call logs, and financial transactions are becoming increasingly available from various fields. These event sequences often exhibit both long-term and short-term temporal dependencies. Variations of neural network based temporal point processes have been widely used for modeling such asynchronous event sequences. However, many current architectures including attention based point processes struggle with long event sequences due to computational inefficiency. To tackle the challenge, we propose an efficient sparse transformer Hawkes process (STHP), which has two components. For the first component, a transformer with a novel temporal sparse self-attention mechanism is applied to event sequences with arbitrary intervals, mainly focusing on short-term dependencies. For the second component, a transformer is applied to the time series of aggregated event counts, primarily targeting the extraction of long-term periodic dependencies. Both components complement each other and are fused together to model the conditional intensity function of a point process for future event forecasting. Experiments on real-world datasets show that the proposed STHP outperforms baselines and achieves significant improvement in computational efficiency without sacrificing prediction performance for long sequences. 
    more » « less