OpenRaiser/Intern-Atlas huggingface dataset:
Neural Predicting Higher-order Patterns in Temporal Networks
Authors: Yunyu Liu, Jianzhu Ma, Pan Li
Venue: WWW
Year: 2022

Dynamic systems that consist of a set of interacting elements can be abstracted as temporal networks. Recently, higher-order patterns that involve multiple interacting nodes have been found crucial to indicate domain-specific laws of different temporal networks. This posts us the challenge of designing more sophisticated hypergraph models for these higher-order patterns and the associated new learning algorithms. Here, we propose the first model, named HIT, for full-spectrum higher-order pattern prediction in temporal hypergraphs. Particularly, we focus on predicting three types of common but important interaction patterns involving three interacting elements in temporal networks, which could be extended to even higher-order patterns. HIT extracts the structural representation of a node triplet of interest on the temporal hypergraph and uses it to tell what type of, when, and why the interaction expansion could happen in this triplet. HIT could achieve significant improvement (averaged 20% AUC gain to identify the interaction type, uniformly more accurate time estimation) compared to both heuristic and other neural-network-based baselines on 5 real-world large temporal hypergraphs. Moreover, HIT provides a certain degree of interpretability by identifying the most discriminatory structural features on the temporal hypergraphs for predicting different higher-order patterns.
Modeling and Optimizing the Scaling Performance in Distributed Deep Learning Training
Authors: Ting Liu, Tianhao Miao, Qinghua Wu, Zhenyu Li, Guangxin He, Jiaoren Wu, Shengzhuo Zhang, Xingwu Yang, Gareth Tyson, Gaogang Xie
Venue: WWW
Year: 2022

Distributed Deep Learning (DDL) is widely used to accelerate deep neural network training for various Web applications. In each iteration of DDL training, each worker synchronizes neural network gradients with other workers. This introduces communication overhead and degrades the scaling performance. In this paper, we propose a recursive model, OSF (Scaling Factor considering Overlap), for estimating the scaling performance of DDL training of neural network models, given the settings of the DDL system. OSF captures two main characteristics of DDL training: the overlap between computation and communication, and the tensor fusion for batching updates. Measurements on a real-world DDL system show that OSF obtains a low estimation error (ranging from 0.5% to 8.4% for different models). Using OSF, we identify the factors that degrade the scaling performance, and propose solutions to effectively mitigate their impacts. Specifically, the proposed adaptive tensor fusion improves the scaling performance by 32.2%∼ 150% compared to the constant tensor fusion buffer size.
Graph-level Semantic Matching model for Knowledge base Aggregate Question Answering
Authors: Ya Liu, Shaojuan Wu, Jiarui Zhang, Linyi Han, Xiaowang Zhang, Yongxin Yu, Zhiyong Feng
Venue: WWW
Year: 2022

In knowledge base question answering, complex question always has long-distance dependencies, especially aggregate question, which affects query graph matching. Many previous approaches have made conspicuous progress in complex question answering. However, they mostly only compare based on the textual similarity of the predicate sequences, ignoring the degree of semantic information either questions or query graphs. In this paper, we propose a Graph-level Semantic Matching (GSM) model to obtain the global semantics representation. Due to the structural complexity of query graphs, we propose a global semantic model to explicitly encode the structural and relational semantics of query graphs. Then, a question-guiding mechanism is applied to enhance the understanding of question semantics in query graph representation. Finally, GSM outperforms existing question answering models, and exhibits capabilities to deal with aggregate questions, e.g., correctly handling counting and comparison in questions.
Towards Unsupervised Deep Graph Structure Learning
Authors: Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan
Venue: WWW
Year: 2022

In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated when noisy connections occur in the original graph structures; besides, the dependence on explicit structures prevents GNNs from being applied to general unstructured scenarios. To address these issues, recently emerged deep graph structure learning (GSL) methods propose to jointly optimize the graph structure along with GNN under the supervision of a node classification task. Nonetheless, these methods focus on a supervised learning scenario, which leads to several problems, i.e., the reliance on labels, the bias of edge distribution, and the limitation on application tasks. In this paper, we propose a more practical GSL paradigm, unsupervised graph structure learning, where the learned graph topology is optimized by data itself without any external guidance (i.e., labels). To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning. Specifically, we generate a learning target from the original data as an “anchor graph”, and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph. To provide persistent guidance, we design a novel bootstrapping mechanism that upgrades the anchor graph with learned structures during model learning. We also design a series of graph learners and post-processing schemes to model the structures to learn. Extensive experiments on eight benchmark datasets demonstrate the significant effectiveness of our proposed SUBLIME and high quality of the optimized graphs.
VICTOR: An Implicit Approach to Mitigate Misinformation via Continuous Verification Reading
Authors: Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku
Venue: WWW
Year: 2022

We design and evaluate VICTOR, an easy-to-apply module on top of a recommender system to mitigate misinformation. VICTOR takes an elegant, implicit approach to deliver fake-news verifications, such that readers of fake news can continuously access more verified news articles about fake-news events without explicit correction. We frame fake-news intervention within VICTOR as a graph-based question-answering (QA) task, with Q as a fake-news article and A as the corresponding verified articles. Specifically, VICTOR adopts reinforcement learning: it first considers fake-news readers’ preferences supported by underlying news recommender systems and then directs their reading sequence towards the verified news articles. To verify the performance of VICTOR, we collect and organize VERI, a new dataset consisting of real-news articles, user browsing logs, and fake-real news pairs for a large number of misinformation events. We evaluate zero-shot and few-shot VICTOR on VERI to simulate the never-exposed-ever and seen-before conditions of users while reading a piece of fake news. Results demonstrate that compared to baselines, VICTOR proactively delivers 6% more verified articles with a diversity increase of 7.5% to over 68% of at-risk users who have been exposed to fake news. Moreover, we conduct a field user study in which 165 participants evaluated fake news articles. Participants in the VICTOR condition show better exposure rates, proposal rates, and click rates on verified news articles than those in the other two conditions. Altogether, our work demonstrates the potentials of VICTOR, i.e., combat fake news by delivering verified information implicitly.
Predicting SPARQL Query Dynamics
Authors: Alberto Moya Loustaunau
Venue: WWW
Year: 2022

The SPARQL language is the recommendation for querying Linked Data, but querying SPARQL endpoints has problems with performance, particularly when clients remotely query SPARQL endpoints over the Web. Traditionally, caching techniques have been used to deal with performance issues by allowing the reuse of intermediate data and results across different queries. However, the resources in Linked Data represent real-world things which change over time. The resources described by these datasets are thus continuously created, moved, deleted, linked, and unlinked, which may lead to stale data in caches. This situation is more critical in the case of applications that consume or interact intensively with Linked Data through SPARQL, including query engines and browsers that constantly send expensive and repetitive queries. Applications that leverage Linked Data could benefit from knowledge about the dynamics of changing query results to efficiently deliver accurate services, since they could refresh at least the dynamic part of the queries. Along these lines, we want to address open questions in terms of assessing the dynamics of SPARQL query results in order to improve the way applications access dynamic Linked Data, making queries more efficient and ensuring fresher results.
Graph Convolutional Networks for Chemical Relation Extraction
Authors: Darshini Mahendran, Christina Tang, Bridget T. McInnes
Venue: WWW
Year: 2022

Extracting information regarding novel chemicals and chemical reactions from chemical patents plays a vital role in the chemical and pharmaceutical industry. Due to the increasing volume of chemical patents, there is an urgent need for automated solutions to extract relations between chemical compounds. Several studies have used models that apply attention mechanisms such as Bidirectional Encoder Representations from Transformers (BERT) to capture the contextual information within a text. However, these models do not capture the global information about a specific vocabulary. On the other hand, Graph Convolutional Networks (GCNs) capture global dependencies between terms within a corpus but not the local contextual information. In this work, we propose two novel approaches, GCN-Vanilla and GCN-BERT, for chemical relation extraction. GCN-Vanilla approach builds a single graph for the whole corpus based on word co-occurrence and sentence-word relations. Then, we model the graph with GCN to capture the global information and classify the sentence nodes. GCN-BERT approach combines GCN and BERT to capture both global and local information, and build together a final representation for relation extraction. We evaluate our approaches on the CLEF-2020 dataset. Our results show the combined GCN-BERT approach outperforms standalone BERT and GCN models, and achieves a higher F1 than that reported in our previous studies.
Culturally Responsive Data Literacy: An Emerging and Important Construct
Authors: Ellen B. Mandinach
Venue: WWW
Year: 2022

Ellen B. Mandianach has given a Keynote Talk at the 3rd Data Literacy Workshop colocated with the Web Conference 2022 on Tuesday 26th April 2022. A summary of the talk titled ”Culturally Responsive Data Literacy: An Emerging and Important Construct” is described in this papers.
Sci-K 2022 - International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment
Authors: Paolo Manghi, Andrea Mannocci, Francesco Osborne, Dimitris Sacharidis, Angelo A. Salatino, Thanasis Vergoulis
Venue: WWW
Year: 2022

In this paper we present the 2nd edition of the Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2022) workshop. Sci-K aims to explore innovative solutions and ideas for the generation of approaches, data models, and infrastructures (e.g., knowledge graphs) for supporting, directing, monitoring and assessing the scientific knowledge and progress. This edition is also a reflection point as the community is seeking alternative solutions to the now-defunct Microsoft Academic Graph (MAG).
Multilingual Semantic Sourcing using Product Images for Cross-lingual Alignment
Authors: Sourab Mangrulkar, Ankith M. S, Vivek Sembium
Venue: WWW
Year: 2022

In online retail stores with ever-increasing catalog, product search is the primary means for customers to discover products of their interest. Surfacing irrelevant products can lead to poor customer experience and in extreme situations loss in engagement. With the recent advances in NLP, Deep Learning models are being used to represent queries and products in shared semantic space to enable semantic sourcing. These models require a lot of human annotated (query, product, relevance) tuples to give competitive results which is expensive to generate. The problem becomes more prominent in the emerging marketplaces/languages due to data paucity problem. When expanding to new marketplaces, it becomes imperative to support regional languages to reach a wider customer base and delighting them with good customer experience. Recently, in the NLP domain, approaches using parallel data corpus for training multilingual models have become prominent, but they are expensive to generate. In this work, we learn semantic alignment across languages using product images as an anchor between them. This overcomes the necessity of parallel data corpus. We use the human annotated data from established marketplace to transfer relevance classification knowledge to new/emerging marketplaces to solve the data paucity problem. Our experiments performed on datasets from Amazon reveal that we outperform state-of-the-art baselines with 2.4%-3.65% ROC-AUC lifts on relevance classification task across non-English marketplaces, 34.69%-51.67% Recall@k lifts on language-agnostic retrieval task and 6.25%-13.42% Precision@k lifts on semantic neighborhood quality task, respectively. Our models demonstrate efficient transfer of relevance classification knowledge from data rich marketplaces to new marketplaces by achieving ROC-AUC lifts of 3.74%-6.25% for the relevance classification task in the zero-shot setting where the human annotated relevance data of target marketplace is unavailable during training.
TRACE: A Fast Transformer-based General-Purpose Lossless Compressor
Authors: Yu Mao, Yufei Cui, Tei-Wei Kuo, Chun Jason Xue
Venue: WWW
Year: 2022

Deep-learning-based compressor has received interests recently due to much improved compression ratio. However, modern approaches suffer from long execution time. To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors. Building history-dependencies sequentially (e.g., recurrent neural networks) is responsible for long inference latency. Instead, we introduce transformer into deep learning compressors to build history-dependencies in parallel. However, existing transformer is too heavy in computation and incompatible to compression tasks. This paper proposes a fast general-purpose lossless compressor, TRACE, by designing a compression-friendly structure based on a single-layer transformer. We first design a new metric to advise the selection part of compression model structures. Byte-grouping and Shared-ffn schemes are further proposed to fully utilize the capacity of the single-layer transformer. These features allow TRACE to achieve competitive compression ratio and a much faster speed. In addition, we further accelerate the compression procedure by designing a controller to reduce the parameter updating overhead. Experiments show that TRACE achieves an overall ∼ 3x speedup while keeps a comparable compression ratio to the state-of-the-art compressors. The source code for TRACE and links to the datasets are available at https://github.com/mynotwo/A-Fast-Transformer-based-General-Purpose-LosslessCompressor.
Towards digital economy through data literate workforce
Authors: Maja Miloradov, Slavko Rakic, Ugljesa Marjanovic
Venue: WWW
Year: 2022

∗In today's digital economy, data are part of everyone's work. Not only decision-makers but also average workers are invited to conduct data-based experiments, interpret data, and create innovative data-based products and services. In this endower, the entire workforce needs additional skills to thrive in this world. This type of competence is united by the name data literacy, and as such, it becomes one of the most valuable skills in the labor market. This paper aims to highlight the needs and shortcomings in terms of competencies for working with data as a critical factor in the business of modern companies striving for digital transformation. Through systematic desk research spanning over 15 European countries, this paper sheds light on how data literacy is addressed in European Higher Education and professional training. In addition, our analysis uses results from an online survey conducted in 20 countries in Europe and North Africa. The results show that the most valuable data literacy competence of an employee is the ability to evaluate or reflect data and the skills related to reading or creating data classification.
Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media
Authors: Erxue Min, Yu Rong, Yatao Bian, Tingyang Xu, Peilin Zhao, Junzhou Huang, Sophia Ananiadou
Venue: WWW
Year: 2022

Fake News detection has attracted much attention in recent years. Social context based detection methods attempt to model the spreading patterns of fake news by utilizing the collective wisdom from users on social media. This task is challenging for three reasons: (1) There are multiple types of entities and relations in social context, requiring methods to effectively model the heterogeneity. (2) The emergence of news in novel topics in social media causes distribution shifts, which can significantly degrade the performance of fake news detectors. (3) Existing fake news datasets usually lack of great scale, topic diversity and user social relations, impeding the development of this field. To solve these problems, we formulate social context based fake news detection as a heterogeneous graph classification problem, and propose a fake news detection model named Post-User Interaction Network (PSIN), which adopts a divide-and-conquer strategy to model the post-post, user-user and post-user interactions in social context effectively while maintaining their intrinsic characteristics. Moreover,we adopt an adversarial topic discriminator for topic-agnostic feature learning, in order to improve the generalizability of our method for new-emerging topics. Furthermore, we curate a new dataset for fake news detection, which contains over 27,155 news from 5 topics, 5 million posts, 2 million users and their induced social graph with 0.2 billion edges. It has been published on https://github.com/qwerfdsaplking/MC-Fake. Extensive experiments illustrate that our method outperforms SOTA baselines in both in-topic and out-of-topic settings.
Graph Representation Learning of Banking Transaction Network with Edge Weight-Enhanced Attention and Textual Information
Authors: Naoto Minakawa, Kiyoshi Izumi, Hiroki Sakaji, Hitomi Sano
Venue: WWW
Year: 2022

In this paper, we propose a novel approach to capture inter-company relationships from banking transaction data using graph neural networks with a special attention mechanism and textual industry or sector information. Transaction data owned by financial institutions can be an alternative source of information to comprehend real-time corporate activities. Such transaction data can be applied to predict stock price and miscellaneous macroeconomic indicators as well as to sophisticate credit and customer relationship management. Although the inter-company relationship is important, traditional methods for extracting information have not captured that enough. With the recent advances in deep learning on graphs, we can expect better extraction of inter-company information from banking transaction data. Especially, we analyze common issues that arise when we represent banking transactions as a network and propose an efficient solution to such problems by introducing a novel edge weight-enhanced attention mechanism, using textual information, and designing an efficient combination of existing graph neural networks.
Second-level Digital Divide: A Longitudinal Study of Mobile Traffic Consumption Imbalance in France
Authors: Sachit Mishra, Zbigniew Smoreda, Marco Fiore
Venue: WWW
Year: 2022

We study the interaction between the consumption of digital services via mobile devices and urbanization levels, using measurement data collected in an operational network serving the whole territory of France. We unveil that such an interaction follows a power law, or, in other words, there exists an emergent behavior that prompts subscribers living in increasingly extended and populated urban areas to exhibit a surging individual consumption of mobile traffic. The result holds for the global traffic, but is also consistently observed across a range of mobile services, although with varying intensity. An unprecedented longitudinal analysis of the phenomenon unveils how the imbalance in the per-capita mobile data traffic usage across cities of different size has grown steadily and substantially in the 2014–2019 time frame in France. Our study raises questions on the presence of second-level digital divides in developed countries, and paves the road to further investigations.
Detecting Addiction, Anxiety, and Depression by Users Psychometric Profiles
Authors: Anna Monreale, Benedetta Iavarone, Elena Rossetto, Andrea Beretta
Venue: WWW
Year: 2022

Detecting and characterizing people with mental disorders is an important task that could help the work of different healthcare professionals. Sometimes, a diagnosis for specific mental disorders requires a long time, possibly causing problems because being diagnosed can give access to support groups, treatment programs, and medications that might help the patients. In this paper, we study the problem of exploiting supervised learning approaches, based on users’ psychometric profiles extracted from Reddit posts, to detect users dealing with Addiction, Anxiety, and Depression disorders. The empirical evaluation shows an excellent predictive power of the psychometric profile and that features capturing the post’s content are more effective for the classification task than features describing the user writing style. We achieve an accuracy of 96% using the entire psychometric profile and an accuracy of 95% when we exclude from the user profile linguistic features.