OpenRaiser/Intern-Atlas huggingface dataset:
MinSearch: An Efficient Algorithm for Similarity Search under Edit Distance
Authors: Haoyu Zhang, Qin Zhang 0001
Venue: KDD
Year: 2020

We study a fundamental problem in data analytics: similarity search under edit distance (or, edit similarity search for short). In this problem we try to build an index on a set of n strings S = s1, ..., sn, with the goal of answering the following two types of queries: (1) the threshold query: given a query string t and a threshold K, output all si ∈ S such that the edit distance between si and t is at most K; (2) the top-k query: given a query string t, output the k strings in S that are closest to t in terms of edit distance. Edit similarity search has numerous applications in bioinformatics, databases, data mining, information retrieval, etc., and has been studied extensively in the literature. In this paper we propose a novel algorithm for edit similarity search named MinSearch. The algorithm is randomized, and we can show mathematically that it outputs the correct answer with high probability for both types of queries. We have conducted an extensive set of experiments on MinSearch, and compared it with the best existing algorithms for edit similarity search. Our experiments show that MinSearch has a clear advantage (often in orders of magnitudes) against the best previous algorithms in query time, and MinSearch is always one of the best among all competitors in the indexing time and space usage. Finally, MinSearch achieves perfect accuracy for both types of queries on all datasets that we have tested.
Parallel DNN Inference Framework Leveraging a Compact RISC-V ISA-based Multi-core System
Authors: Yipeng Zhang 0001, Bo Du 0001, Lefei Zhang, Jia Wu 0001
Venue: KDD
Year: 2020

RISC-V is an open-source instruction set and now has been examined as a universal standard to unify the heterogeneous platforms. However, current research focuses primarily on the design and fabrication of general-purpose processors based on RISC-V, despite the fact that in the era of IoT (Internet of Things), the fusion of heterogeneous platforms should also take application-specific processors into account. Accordingly, this paper proposes a collaborative RISC-V multi-core system for Deep Neural Network (DNN) accelerators. To the best of our knowledge, this is the first time that a multi-core scheduling architecture for DNN acceleration is formulated and RISC-V is explored as the ISA of a multi-core system to bridge the gap between the memory and the DNN Processor in order to increase the entire system throughput. The experiment realizes a four-stage design of the RISC-V core, and further reveals that a multi-core design along with an appropriate scheduling algorithm can efficiently decrease the runtime and elevate the throughput. Moreover, the experiment also provides us with a constructive suggestion regarding the ideal proportion of the cores to Process Engines (PE), which provides us with significant assistance in building highly efficient AI System-on-Chips (SoCs) in resource-aware situations.
Multi-modal Network Representation Learning
Authors: Chuxu Zhang, Meng Jiang 0001, Xiangliang Zhang 0001, Yanfang Ye 0001, Nitesh V. Chawla
Venue: KDD
Year: 2020

In today's information and computational society, complex systems are often modeled as multi-modal networks associated with heterogeneous structural relation, unstructured attribute/content, temporal context, or their combinations. The abundant information in multi-modal network requires both a domain understanding and large exploratory search space when doing feature engineering for building customized intelligent solutions in response to different purposes. Therefore, automating the feature discovery through representation learning in multi-modal networks has become essential for many applications. In this tutorial, we systematically review the area of multi-modal network representation learning, including a series of recent methods and applications. These methods will be categorized and introduced in the perspectives of unsupervised, semi-supervised and supervised learning, with corresponding real applications respectively. In the end, we conclude the tutorial and raise open discussions. The authors of this tutorial are active and productive researchers in this area.
Prediction and Profiling of Audience Competition for Online Television Series
Authors: Peng Zhang 0001, Chuanren Liu, Kefeng Ning, Wenxiang Zhu, Yu Zhang
Venue: KDD
Year: 2020

Understanding the target audience for popular television series is valuable for online video platform to manage advertising sales, purchase video copyrights, and compete with other video service platforms. Existing studies in this domain generally focus on using data mining and machine learning techniques to recommend television series to individual users or predict the popularity of television series. Knowing only the popularity of television series may, however, limit our ability to answer more in-depth questions and develop more intelligent applications. In this paper, we develop a data-driven framework to model and predict audience competition patterns for popular online television series. Specifically, we first construct a sequence of dynamic competition networks of television series by mining the detailed viewership records. Then, we design the Dynamic Deep Network Factorization (DDNF), a hybrid modeling framework for predicting the future competition networks. Our framework adopts the deep neural network (DNN) and the knowledge-base (KB) embedding to incorporate static features, and integrates the Long Short-Term Memory (LSTM) network to learn dynamic features of the television series. Finally, extensive experiments on real-world data sets validate the effectiveness of our approach compared with state-of-the-art baselines in predicting the audience competition for existing and new television series.
Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks
Authors: Yingxue Zhang 0002, Yanhua Li, Xun Zhou 0001, Xiangnan Kong, Jun Luo 0007
Venue: KDD
Year: 2020

Given an urban development plan and the historical traffic observations over the road network, the Conditional Urban Traffic Estimation problem aims to estimate the resulting traffic status prior to the deployment of the plan. This problem is of great importance to urban development and transportation management, yet is very challenging because the plan would change the local travel demands drastically and the new travel demand pattern might be unprecedented in the historical data. To tackle these challenges, we propose a novel Conditional Urban Traffic Generative Adversarial Network (Curb-GAN), which provides traffic estimations in consecutive time slots based on different (unprecedented) travel demands, thus enables urban planners to accurately evaluate urban plans before deploying them. The proposed Curb-GAN adopts and advances the conditional GAN structure through a few novel ideas: (1) dealing with various travel demands as the "conditions" and generating corresponding traffic estimations, (2) integrating dynamic convolutional layers to capture the local spatial auto-correlations along the underlying road networks, (3) employing self-attention mechanism to capture the temporal dependencies of the traffic across different time slots. Extensive experiments on two real-world spatio-temporal datasets demonstrate that our Curb-GAN outperforms major baseline methods in estimation accuracy under various conditions and can produce more meaningful estimations.
NetTrans: Neural Cross-Network Transformation
Authors: Si Zhang, Hanghang Tong, Yinglong Xia, Liang Xiong, Jiejun Xu
Venue: KDD
Year: 2020

Finding node associations across different networks is the cornerstone behind a wealth of high-impact data mining applications. Traditional approaches are often, explicitly or implicitly, built upon the linearity and/or consistency assumptions. On the other hand, the recent network embedding based methods promise a natural way to handle the non-linearity, yet they could suffer from the disparate node embedding space of different networks. In this paper, we address these limitations and tackle cross-network node associations from a new angle, i.e., cross-network transformation. We ask a generic question: Given two different networks, how can we transform one network to another? We propose an end-to-end model that learns a composition of nonlinear operations so that one network can be transformed to another in a hierarchical manner. The proposed model bears three distinctive advantages. First (composite transformation), it goes beyond the linearity/consistency assumptions and performs the cross-network transformation through a composition of nonlinear computations. Second (representation power), it can learn the transformation of both network structures and node attributes at different resolutions while identifying the cross-network node associations. Third (generality), it can be applied to various tasks, including network alignment, recommendation, cross-layer dependency inference. Extensive experiments on different tasks validate and verify the effectiveness of the proposed model.
Scalable Graph Neural Networks with Deep Graph Library
Authors: Da Zheng 0004, Minjie Wang, Quan Gan, Zheng Zhang 0001, George Karypis
Venue: KDD
Year: 2020

Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, e-commerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. In practice, many of the real-world graphs are very large. It is urgent to have scalable solutions to train GNN on large graphs efficiently.
The NodeHopper: Enabling Low Latency Ranking with Constraints via a Fast Dual Solver
Authors: Anton Zhernov, Krishnamurthy (Dj) Dvijotham, Ivan Lobov, Dan A. Calian, Michelle X. Gong, Natarajan Chandrashekar, Timothy A. Mann
Venue: KDD
Year: 2020

Modern recommender systems need to deal with multiple objectives like balancing user engagement with recommending diverse and fresh content. An appealing way to optimally trade these off is by imposing constraints on the ranking according to which items are presented to a user. This results in a constrained ranking optimization problem that can be solved as a linear program (LP). However, off-the-shelf LP solvers are unable to meet the severe latency constraints in systems that serve live traffic. To address this challenge, we exploit the structure of the dual optimization problem to develop a fast solver. We analyze theoretical properties of our solver and show experimentally that it is able to solve constrained ranking problems on synthetic and real-world recommendation datasets an order of magnitude faster than off-the-shelf solvers, thereby enabling their deployment under severe latency constraints.
HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification
Authors: Zhihua Zhu, Xinxin Fan, Xiaokai Chu, Jingping Bi
Venue: KDD
Year: 2020

Collective classification, as an important technique to study networked data, aims to exploit the label autocorrelation for a group of inter-connected entities with complex dependencies. As the emergence of various heterogeneous information networks (HINs), collective classification at present is confronting several severe challenges stemming from the heterogeneity of HINs, such as complex relational hierarchy, potential incompatible semantics and node-context relational semantics. To address the challenges, in this paper, we propose a novel heterogeneous graph convolutional network-based deep learning model, called HGCN, to collectively categorize the entities in HINs. Our work involves three primary contributions: i) HGCN not only learns the latent relations from the relation-sophisticated HINs via multi-layer heterogeneous convolutions, but also captures the semantic incompatibility among relations with properly-learned edge-level filter parameters; ii) to preserve the fine-grained relational semantics of different-type nodes, we propose a heterogeneous graph convolution to directly tackle the original HINs without any in advance transforming the network from heterogeneity to homogeneity; iii) we perform extensive experiments using four real-world datasets to validate our proposed HGCN, the multi-facet results show that our proposed HGCN can significantly improve the performance of collective classification compared with the state-of-the-art baseline methods.
Order Fulfillment Cycle Time Estimation for On-Demand Food Delivery
Authors: Lin Zhu, Wei Yu, Kairong Zhou, Xing Wang, Wenxing Feng, Pengyu Wang 0005, Ning Chen 0007, Pei Lee
Venue: KDD
Year: 2020

By providing customers with conveniences such as easy access to an extensive variety of restaurants, effortless food ordering and fast delivery, on-demand food delivery (OFD) platforms have achieved explosive growth in recent years. A crucial machine learning task performed at OFD platforms is prediction of the Order Fulfillment Cycle Time (OFCT), which refers to the amount of time elapsed between a customer places an order and he/she receives the meal. The accuracy of predicted OFCT is important for customer satisfaction, as it needs to be communicated to a customer before he/she places the order, and is considered as a service promise that should be fulfilled as well as possible. As a result, the estimated OFCT also heavily influences planning decisions such as dispatching and routing.
Certifiable Robustness of Graph Convolutional Networks under Structure Perturbations
Authors: Daniel Zügner, Stephan Günnemann
Venue: KDD
Year: 2020

Recent works show that message-passing neural networks (MPNNs) can be fooled by adversarial attacks on both the node attributes and the graph structure. Since MPNNs are currently being rapidly adopted in real-world applications, it is thus crucial to improve their reliablility and robustness. While there has been progress on robustness certification of MPNNs under perturbation of the node attributes, no existing method can handle structural perturbations. These perturbations are especially challenging because they alter the message passing scheme itself. In this work we close this gap and propose the first method to certify robustness of Graph Convolutional Networks (GCNs) under perturbations of the graph structure. We show how this problem can be expressed as a jointly constrained bilinear program - a challenging, yet well-studied class of problems - and propose a novel branch-and-bound algorithm to obtain lower bounds on the global optimum. These lower bounds are significantly tighter and can certify up to twice as many nodes compared to a standard linear relaxation.
On the Power of Pre-Trained Text Representations: Models and Applications in Text Mining
Authors: Yu Meng, Jiaxin Huang, Yu Zhang, Jiawei Han
Venue: KDD
Year: 2021

Recent years have witnessed the enormous success of text representation learning in a wide range of text mining tasks. Earlier word embedding learning approaches represent words as fixed low-dimensional vectors to capture their semantics. The word embeddings so learned are used as the input features of task-specific models. Recently, pre-trained language models (PLMs), which learn universal language representations via pre-training Transformer-based neural models on large-scale text corpora, have revolutionized the natural language processing (NLP) field. Such pre-trained representations encode generic linguistic features that can be transferred to almost any text-related applications. PLMs outperform previous task-specific models in many applications as they only need to be fine-tuned on the target corpus instead of being trained from scratch. In this tutorial, we introduce recent advances in pre-trained text embeddings and language models, as well as their applications to a wide range of text mining tasks. Specifically, we first overview a set of recently developed self-supervised and weakly-supervised text embedding methods and pre-trained language models that serve as the fundamentals for downstream tasks. We then present several new methods based on pre-trained text embeddings and language models for various text mining applications such as topic discovery and text classification. We focus on methods that are weakly-supervised, domain-independent, language-agnostic, effective and scalable for mining and discovering structured knowledge from large-scale text corpora. Finally, we demonstrate with real-world datasets how pre-trained text representations help mitigate the human annotation burden and facilitate automatic, accurate and efficient text analyses.
ODD: Outlier Detection and Description
Authors: Siddharth Bhatia, Bryan Hooi, Leman Akoglu, Sourav Chatterjee, Xiaodong Jiang, Manish Gupta
Venue: KDD
Year: 2021

We propose to organize the 6th ODD workshop at KDD 2021, following the successful series of the past five ODD Workshops that have been organized at KDD 2013, KDD 2014, KDD 2015, KDD 2016, and KDD 2018.
Weakly Supervised Spatial Deep Learning based on Imperfect Vector Labels with Registration Errors
Authors: Zhe Jiang, Wenchong He, Marcus Stephen Kirby, Sultan Asiri, Da Yan
Venue: KDD
Year: 2021

This paper studies weakly supervised learning on spatial raster data based on imperfect vector training labels. Given raster feature imagery and imperfect (weak) vector labels with location registration errors, our goal is to learn a deep learning model for pixel classification and refine vector labels simultaneously. The problem is important in many geoscience applications such as streamline delineation and road mapping from earth imagery, where annotating imperfect coarse vector labels is far more efficient than drawing precise labels. But the problem is challenging due to the misalignment of vector labels with raster feature pixels and the need to infer true vector label location while learning neural network parameters. Existing works on weakly supervised learning often focus on noise and errors in label semantics, assuming label locations to be either correct or irrelevant (e.g., identical and independently distributed). A few works exist on label registration errors, but these methods often focus on label misalignment on object segment boundaries at the pixel level without guaranteeing vector continuity. To fill the gap, this paper proposes a spatial learning framework based on Expectation-Maximization that iteratively updates deep neural network parameters while inferring true vector label locations. Specifically, inference of true vector locations is based on both the current pixel class predictions and the geometric properties of vectors. Evaluations on real-world high-resolution remote sensing datasets in National Hydrography Dataset (NHD) refinement show that the proposed framework outperforms baseline methods in classification accuracy and refined vector quality.
From Deep Learning to Deep Reasoning
Authors: Truyen Tran, Vuong Le, Hung Le, Thao Minh Le
Venue: KDD
Year: 2021

The rise of big data and big compute has brought modern neural networks to many walks of digital life, thanks to the relative ease of constructing large models that scale to the real world. Current successes of Transformers and self-supervised pretraining on massive data have led some to believe that deep neural networks will be able to do almost everything once we have sufficient data and computational resources. However, neural networks are fast to exploit surface statistics but fail miserably to generalize to novel combinations. This is because they are not designed for deliberate reasoning -- the capacity to deliberately deduce new knowledge out of the contextualized data. This tutorial reviews recent developments to extend the capacity of neural networks to "learning-to-reason'' from data, where the task is to determine if the data entails a conclusion. This capacity opens up new ways to generate insights from data through arbitrary compositional querying without the need of predefining a narrow set of tasks. The tutorial consists of four parts. The first part covers the learning-to-reason framework, and explains how neural networks can serve as a strong backbone for reasoning through its natural operations such as binding, attention & dynamic computational graphs. The second part goes into more detail on how neural networks perform reasoning over unstructured and structured data, and across modalities. The third part reviews neural memories and their role in reasoning. The last part discusses generalization to novel combinations, under less supervision and with more knowledge.
Metric Learning via Penalized Optimization
Authors: Hao Huang, Yanan Peng, Ting Gan, Weiping Tu, Ruiting Zhou, Sai Wu
Venue: KDD
Year: 2021

Metric learning aims to project original data into a new space, where data points can be classified more accurately using kNN or similar types of classification algorithms. To avoid trivial learning results such as indistinguishably projecting the data onto a line, many existing approaches formulate metric learning as a constrained optimization problem, like finding a metric that minimizes the distance between data points from the same class, with a constraint of ensuring a certain separation for data points from different classes, and then they approximate the optimal solution to the constrained optimization in an iterative way. In order to improve the classification accuracy as much as possible, we try to find a metric that is able to minimize the intra-class distance and maximize the inter-class distance simultaneously. Towards this, we formulate metric learning as a penalized optimization problem, and provide design guideline, paradigms with a general formula, as well as two representative instantiations for the penalty term. In addition, we provide an analytical solution for the penalized optimization, with which costly computation can be avoid, and more importantly, there is no need to worry about the convergence rates or approximation ratios any more. Extensive experiments on real-world data sets are conducted, and the results verify the effectiveness and efficiency of our approach.
20th International Workshop on Data Mining in Bioinformatics (BIOKDD 2021)
Authors: Da Yan, Steve Qin, Debswapna Bhattacharya, Jake Y. Chen, Mohammed J. Zaki
Venue: KDD
Year: 2021

The goal of the 20th International Workshop on Data Mining in Bioinformatics (BIOKDD 2021) is to encourage KDD researchers to tackle the numerous problems and challenges in Bioinformatics using Data Mining technologies. Based on the organizers' expertise and the BIOKDD communities, BIOKDD 2021 features the theme of "Artificial Intelligence in Medicine". This topic focuses on the use of machine learning and data mining techniques for the analysis of large amounts of heterogeneous, complex, biological and medical data, with a particular focus on deep learning methods that have seen rapid advance and wider adoption in Bioinformatics (e.g., DeepVariant, AlphaFold 2). We also particularly welcome COVID-19 related research. The key goal is to accelerate the convergence between Data Mining and Bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare.