The scientific impact of academic papers is influenced by intricate factors such as dynamic popularity and inherent contribution. Existing models typically rely on static graphs for citation count estimation, failing to differentiate among its sources. In contrast, we propose distinguishing effects derived from various factors and predicting citation increments as estimated potential impacts within the dynamic context. In this research, we introduce a novel model, DPPDCC, which Disentangles the Potential impacts of Papers into Diffusion, Conformity, and Contribution values. It encodes temporal and structural features within dynamic heterogeneous graphs derived from the citation networks and applies various auxiliary tasks for disentanglement. By emphasizing comparative and co-cited/citing information and aggregating snapshots evolutionarily, DPPDCC captures knowledge flow within the citation network. Afterwards, popularity is outlined by contrasting augmented graphs to extract the essence of citation diffusion and predicting citation accumulation bins for quantitative conformity modeling. Orthogonal constraints ensure distinct modeling of each perspective, preserving the contribution value. To gauge generalization across publication times and replicate the realistic dynamic context, we partition data based on specific time points and retain all samples without strict filtering. Extensive experiments on three datasets validate DPPDCC's superiority over baselines for papers published previously, freshly, and immediately, with further analyses confirming its robustness. Our codes and supplementary materials can be found at GitHub (https://github.com/ECNU-Text-Computing/DPPDCC).

Accurately evaluating the impact of scientific papers is crucial. However, existing methodologies face certain challenges, including latent factors affecting citation behaviors and dynamic intrinsic of citation networks. To address these challenges, this study introduces a novel framework named CoDy (modeling scholarly Collaboration and temporal Dynamics in citation networks for impact prediction). CoDy strategically predicts author collaborations as an auxiliary task, forecasting not only the number of current collaborations between scholars but also the number of future collaborations among them. Besides, CoDy proposes a fine-grained temporal encoding module to model the multiple different temporal patterns for publication and citation. Extensive experimental validations demonstrate CoDy's effectiveness in predicting citation counts and classifying impact levels. In-depth analyses provide further validation of its reliability and robustness. CoDy can significantly enhance impact prediction by explicitly modeling collaboration and temporal patterns and offer valuable insights into paper impact formation.

Cyberspace hosts abundant interactions between users and different kinds of objects, and their relations are often encapsulated as bipartite graphs. Detecting user community in such heterogeneous graphs is an essential task to uncover user information needs and to further enhance recommendation performance. While several main cyber domains carrying high-quality graphs, unfortunately, most others can be quite sparse. However, as users may appear in multiple domains (graphs), their high-quality activities in the main domains can supply community detection in the sparse ones, e.g., user behaviors on Google can help thousands of applications to locate his/her local community when s/he uses Google ID to login those applications. In this paper, our model, Pairwise Cross-graph Community Detection (PCCD), is proposed to cope with the sparse graph problem by involving external graph knowledge to learn user pairwise community closeness instead of detecting direct communities. Particularly in our model, to avoid taking excessive propagated information, a two-level filtering module is utilized to select the most informative connections through both community and node level filters. Subsequently, a Community Recurrent Unit (CRU) is designed to estimate pairwise user community closeness. Extensive experiments on two real-world graph datasets validate our model against several strong alternatives. Supplementary experiments also validate its robustness on graphs with varied sparsity scales.

Implied semantics is a complex language act that can appear everywhere on the Cyberspace. The prevalence of implied spam texts, such as implied pornography, sarcasm, and abuse hidden within the novel, tweet, microblog, or review, can be extremely harmful to the physical and mental health of teenagers. The non-literal interpretation of the implied text is hard to be understood by machine models due to its high context-sensitivity and heavy usage of figurative language. In this study, inspired by human reading comprehension, we propose a novel, simple, and effective deep neural framework, called Skim and Intensive Reading Model (SIRM), for figuring out implied textual meaning. The proposed SIRM consists of three main components, namely the skim reading component, intensive reading component, and adversarial training component. N-gram features are quickly extracted from the skim reading component, which is a combination of several convolutional neural networks, as skim (entire) information. An intensive reading component enables a hierarchical investigation for both sentence-level and paragraph-level representation, which encapsulates the current (local) embedding and the contextual information (context) with a dense connection. More specifically, the contextual information includes the near-neighbor information and the skim information mentioned above. Finally, besides the common training loss function, we employ an adversarial loss function as a penalty over the skim reading component to eliminate noisy information (noise) arisen from special figurative words in the training data. To verify the effectiveness, robustness, and efficiency of the proposed architecture, we conduct extensive comparative experiments on an industrial novel dataset involving implied pornography and three sarcasm benchmarks. Experimental results indicate that (1) the proposed model, which benefits from context and local modeling and consideration of figurative language (noise), outperforms existing state-of-the-art solutions, with comparable parameter scale and running speed; (2) the SIRM yields superior robustness in terms of parameter size sensitivity; (3) compared with ablation and addition variants of the SIRM, the final framework is efficient enough.

Technological advancements have led to increasing availability of erotic literature and pornography novels online, which can be alluring to adolescence and children. Unfortunately, because of the inherent complexity of these indecent contents and training data sparseness, it is a challenging task to detect these readings in the Cyberspace while children can easily access them. In this study, we propose a novel framework, Joint Learning of Content and Human Attention (GoodMan), to identify indecent readings by augmenting natural language understanding models with large scale human reading behaviors (dwell time per page) on portable devices. From the text modeling viewpoint, the innovative joint attention trained by joint learning is employed to orchestrate the content attention and human behavior attention via the BiGRU. From the data augmentation perspective, various users' reading behaviors on the same text can generate considerable training instances with joint attention, which can be effective to address the cold start problem. We conduct an extensive set of experiments on an online ebook dataset (with human reading behaviors on portable devices). The experimental results show insights into the task and demonstrate the superiority of the proposed model against alternative solutions.

The recent success of deep graph embedding innovates the graphical information characterization methodologies. However, in real-world applications, such a method still struggles with the challenges of heterogeneity, scalability, and multiplex. To address these challenges, in this study, we propose a novel solution, Genetic hEterogeneous gRaph eMbedding (GERM), which enables flexible and efficient task-driven vertex embedding in a complex heterogeneous graph. Unlike prior efforts for this track of studies, we employ a task-oriented genetic activation strategy to efficiently generate the “Edge Type Activated Vector” (ETAV) over the edge types in the graph. The generated ETAV can not only reduce the incompatible noise and navigate the heterogeneous graph random walk at the graph-schema level, but also activate an optimized subgraph for efficient representation learning. By revealing the correlation between the graph structure and task information, the model interpretability can be enhanced as well. Meanwhile, an activated heterogeneous skip-gram framework is proposed to encapsulate both topological and task-specific information of a given heterogeneous graph. Through extensive experiments on both scholarly and e-commerce datasets, we demonstrate the efficacy and scalability of the proposed methods via various search/recommendation tasks. GERM can significantly reduces the running time and remove expert-intervention without sacrificing the performance (or even modestly improve) by comparing with baselines.

Aspect category detection is an essential task for sentiment analysis and opinion mining. However, the cost of categorical data labeling, e.g., label the review aspect information for a large number of product domains, can be inevitable but unaffordable. In this study, we propose a novel problem, cross-domain aspect category transfer and detection, which faces three challenges: various feature spaces, different data distributions, and diverse output spaces. To address these problems, we propose an innovative solution, Traceable Heterogeneous Graph Representation Learning (THGRL). Unlike prior text-based aspect detection works, THGRL explores latent domain aspect category connections via massive user behavior information on a heterogeneous graph. Moreover, an innovative latent variable "Walker Tracer" is introduced to characterize the global semantic/aspect dependencies and capture the informative vertexes on the random walk paths. By using THGRL, we project different domains' feature spaces into a common one, while allowing data distributions and output spaces stay differently. Experiment results show that the proposed method outperforms a series of state-of-the-art baseline models.

It is an important and urgent research problem for decentralized eCommerce services, e.g., eBay, eBid, and Taobao, to detect illegal products, e.g., unclassified pornographic products. However, it is a challenging task as some sellers may utilize and change camouflaged text to deceive the current detection algorithms. In this study, we propose a novel task to dynamically locate the pornographic products from very large product collections. Unlike prior product classification efforts focusing on textual information, the proposed model, BerryPIcking TRee MoDel (BIRD), utilizes both product textual content and buyers' seeking behavior information as berrypicking trees. In particular, the BIRD encodes both semantic information with respect to all branches sequence and the overall latent buyer intent during the whole seeking process. An extensive set of experiments have been conducted to demonstrate the advantage of the proposed model against alternative solutions. To facilitate further research of this practical and important problem, the codes and buyers' seeking behavior data have been made publicly available1.

Although the scientific digital library is growing at a rapid pace, scholars/students often find reading Science, Technology, Engineering, and Mathematics (STEM) literature daunting, especially for the math-content/formula. In this paper, we propose a novel problem, "mathematics content understanding", for cyberlearning and cyberreading. To address this problem, we create a Formula Evolution Map (FEM) offline and implement a novel online learning/reading environment, PDF Reader with Math-Assistant (PRMA), which incorporates innovative math-scaffolding methods. The proposed algorithm/system can auto-characterize student emerging math-information need while reading a paper and enable students to readily explore the formula evolution trajectory in FEM. Based on a math-information need, PRMA utilizes innovative joint embedding, formula evolution mining, and heterogeneous graph mining algorithms to recommend high quality Open Educational Resources (OERs), e.g., video, Wikipedia page, or slides, to help students better understand the math-content in the paper. Evaluation and exit surveys show that the PRMA system and the proposed formula understanding algorithm can effectively assist master and PhD students better understand the complex math-content in the class readings.

While the volume of scholarly publications has increased at a frenetic pace, accessing and consuming the useful candidate papers, in very large digital libraries, is becoming an essential and challenging task for scholars. Unfortunately, because of language barrier, some scientists (especially the junior ones or graduate students who do not master other languages) cannot efficiently locate the publications hosted in a foreign language repository. In this study, we propose a novel solution, cross-language citation recommendation via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to address this new problem. HRLHG can learn a representation function by mapping the publications, from multilingual repositories, to a low-dimensional joint embedding space from various kinds of vertexes and relations on a heterogeneous graph. By leveraging both global (task specific) plus local (task independent) information as well as a novel supervised hierarchical random walk algorithm, the proposed method can optimize the publication representations by maximizing the likelihood of locating the important cross-language neighborhoods on the graph. Experiment results show that the proposed method can not only outperform state-of-the-art baseline models, but also improve the interpretability of the representation model for cross-language citation recommendation task.

Citation metadata extraction plays an important role in academic information retrieval and knowledge management. Current works on this task generally use rule-based, template-based or learning-based approaches but these methods usually either rely on handcrafted features or are limited with domains. Recently, neural networks have shown strong ability in addressing sequence labeling tasks.

This paper presents the results of ICDAR2017 Competition on Page Object Detection (POD). POD is to detect page objects (tables, mathematical equations, graphics, figures, etc.) from document images. This competition makes use of a dataset consists of 2,000 document page images This dataset contains abundant page objects with various types and layouts. During the competition, we received 13 different teams' registrations and finally 8 of them submitted their results. All teams used deep learning as the basic method, then combined different traditional features or methods to improve the detection performance. The team NLPR-PAL achieved the averaged F1 of 0.898 and mAPs of 0.805 in the detection of all page objects under the IOU threshold 0.8. In this overview paper, we summarize the task design, dataset, results, and the approaches used by those teams of this competitions.

In practice, PDF files may be generated by different tools and their character information quality could be different. As a result, the approaches to detecting formulae from PDF documents usually have much different performance on different PDF files. To address this problem, in this paper we combine and refine the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model to detect formulae according to both their character and vision features. Based on the characteristic of PDF documents, we propose a series of strategies to train and optimize deep networks, such as the implicit class down-sampling strategy which can reduce the unbalancedness between formulae and other page elements (e.g., text paragraphs, tables, figures, etc.). The region proposal method is also redesigned to generate moderate formula candidates through combining the bottom-up and top-down layout analysis. The experimental results show that the combination of CNN and RNN can increase the robustness of our proposed detection method. Furthermore, the proposed method outperforms the existing formula detection methods on both a ground-truth dataset and a larger self-built dataset, which would be released and available for research purposes.

This electronic document is a "live" template. The various components of your paper [title, text, heads, etc.] are Abstract-Object detection in natural scenes has been widely researched in the past decade, and many deep learning based methods have achieved good performance on this task. This paper focuses on how to transfer and refine those object detection approaches from natural scene images to documents images, and proposes a deep learning-based page object (e.g., tables, formulae, figures) detection method. On the basis of traditional Convolutional Neural Network (CNN) based object detection methods, we redesign the region proposal method, the training strategy, the network structure and replace the Non-Maximum Suppression (NMS) with a dynamic programming algorithm. The experimental results show that it is essential to adjust some modules of the natural scene object detection approaches in order to better process the document images. The proposed method also achieved better performance compared with existing page object detection methods.

With more and more scientific documents becoming available in PDF format, recognition of formulae in these PDF documents is of great significance. In this paper, we propose a symbol dominance based formulae recognition approach to recovering formulae structures by using the rich information extracted directly from PDF files. The hierarchical structure of formula is represented by relationship tree, and the tree is built recursively based on symbol dominance, which considers both the spatial layout of symbols and the typesetting conventions of mathematics. In addition, we propose a special character recognition method to identify the formula characters with multiple components or variable unicode. Repeatable and comparable experiments have been done over two large datasets, IM2LATEX-100K and PDFME-10K. Experimental results demonstrate that our method is more adaptive and practical for PDF documents compared with other two existing available formulae recognition systems, INFTY and WYGIWYS.

Although the content in scientific publications is increasingly challenging, it is necessary to investigate another important problem, that of scientific information understanding. For this proposed problem, we investigate novel methods to assist scholars (readers) to better understand scientific publications by enabling physical and virtual collaboration. For physical collaboration, an algorithm will group readers together based on their profiles and reading behavior, and will enable the cyberreading collaboration within a online reading group. For virtual collaboration, instead of pushing readers to communicate with others, we cluster readers based on their estimated information needs. For each cluster, a learning to rank model will be generated to recommend readers' communitized resources (i.e., videos, slides, and wikis) to help them understand the target publication.

The citation relationships between publications, which are significant for assessing the importance of scholarly components within a network, have been used for various scientific applications. Missing citation metadata in scholarly databases, however, create problems for classical citation‐based ranking algorithms and challenge the performance of citation‐based retrieval systems. In this research, we utilize a two‐step citation analysis method to investigate the importance of publications for which citation information is partially missing. First, we calculate the importance of the author and then use his importance to estimate the publication importance for some selected articles. To evaluate this method, we designed a simulation experiment—“random citation‐missing”—to test the two‐step citation analysis that we carried out with the Association for Computing Machinery (ACM) Digital Library (DL). In this experiment, we simulated different scenarios in a large‐scale scientific digital library, from high‐quality citation data, to very poor quality data, The results show that a two‐step citation analysis can effectively uncover the importance of publications in different situations. More importantly, we found that the optimized impact from the importance of an author (first step) is exponentially increased when the quality of citation decreases. The findings from this study can further enhance citation‐based publication‐ranking algorithms for real‐world applications.

As the volume of publications has increased dramatically, an urgent need has developed to assist researchers in locating high-quality, candidate-cited papers from a research repository. Traditional scholarly-recommendation approaches ignore the chronological nature of citation recommendations. In this study, we propose a novel method called "Chronological Citation Recommendation" which assumes initial user information needs could shift while users are searching for papers in different time slices. We model the information-need shifts with two-level modeling: dynamic time-related ranking feature construction and dynamic evolving feature weight training. In more detail, we employed a supervised document influence model to characterize the content "time-varying" dynamics and constructed a novel heterogeneous graph that encapsulates dynamic topic-based information, time-decay paper/topic citation information, and word-based information. We applied multiple meta-paths for different ranking hypotheses which carried different types of information for citation recommendation in various time slices, along with information-need shifting. We also used multiple learning-to-rank models to optimize the feature weights for different time slices to generate the final "Chronological Citation Recommendation" rankings. The use of Chronological Citation Recommendation suggests time-series ranking lists based on initial user textual information need and characterizes the information-need shifting. Experiments on the ACM corpus show that Chronological Citation Recommendation can significantly enhance citation recommendation performance.

Scientific publication retrieval/recommendation has been investigated in the past decade. However, to the best of our knowledge, few efforts have been made to help junior scholars and graduate students to understand and consume the essence of those scientific readings. This paper proposes a novel learning/reading environment, OER-based Collaborative PDF Reader (OCPR), that incorporates innovative scaffolding methods that can: 1. auto-characterize student emerging information need while reading a paper; and 2. enable students to readily access open educational resources (OER) based on their information need. By using metasearch methods, we pre-indexed 1,112,718 OERs, including presentation videos, slides, algorithm source code, or Wikipedia pages, for 41,378 STEM publications. Based on the computational information need, we use text mining and heterogeneous graph mining algorithms to recommend high quality OERs to help students better understand the scientific content in the paper. Evaluation results and exit surveys for an information retrieval course show that the OCPR system alone with the recommended OERs can effectively assist graduate students better understand the complex STEM publications. For instance, 78.42% of participants believe the OCPR system and recommended OERs can provide precise and useful information they need, while 78.43% of them believe the recommended OERs are close to exactly what they need when reading the paper. From OER ranking viewpoint, MRR, MAP and NDCG results prove that learning to rank and cold start solutions can efficiently integrate different text and graph ranking features.

Scientific information recommendation is crucial to assist scholars for their researches. Citation recommendation is an important field of scientific recommendation. Traditional approaches ignore the chronological nature of the citation recommendation task. In this study, I propose the "Chronological Citation Recommendation," which assumes initial user information need could shift while they are looking for the papers in different time slices. Specifically, I employed a supervised dynamic topic model to characterize the content "time-varying" dynamics and constructed a novel heterogeneous graph that contains dynamic topic-based information, time-decay citation information and word-based information. I applied different meta-paths for different ranking hypotheses, which carried different types of information for citation recommendation in different time slices along with information need shifting. I plan to generate the final "Chronological Citation Recommendation" rankings by feature integration using Learning to Rank. "Chronological Citation Recommendation" will recommend time-series ranking lists based on initial user textual information need. Preliminary experiments on the ACM corpus show that chronological citation recommendation will significantly improve the citation recommendation performance.