OpenRaiser/Intern-Atlas huggingface dataset:
From Zero to AI Hero with Automated Machine Learning
Authors: Aniththa Umamahesan, Deepak Mukunthu Iyappan Babu
Venue: KDD
Year: 2020

Automated ML is an emerging field in Machine Learning that helps developers and new data scientists with little data science knowledge build Machine Learning models and solutions without understanding the complexity of Learning Algorithm selection, and Hyper parameter tuning. With Azure Machine Learning's automated machine learning capability, given a dataset and a few configuration parameters, you will get a trained high quality machine learning model for the dataset that you can use for predictions. In this session, you will learn how to use Automated ML for productivity gains, empowering domain experts to build ML based solutions and scale to build several models with Azure Machine Learning's Automated ML.
The Dark Side of Machine Learning Algorithms: How and Why They Can Leverage Bias, and What Can Be Done to Pursue Algorithmic Fairness
Authors: Mariya I. Vasileva
Venue: KDD
Year: 2020

Machine learning and access to big data are revolutionizing the way many industries operate, providing analytics and automation to many aspects of real-world practical tasks that were previously thought to be necessarily manual. With the pervasiveness of artificial intelligence and machine learning over the past decade, and their epidemic spread in a variety of applications, algorithmic fairness has become a prominent open research problem. For instance, machine learning is used in courts to assess the probability that a defendant recommits a crime; in the medical domain to assist with diagnosis or predict predisposition to certain diseases; in social welfare systems; and autonomous vehicles. The decision making processes in these real-world applications have a direct effect on people's lives, and can cause harm to society if the machine learning algorithms deployed are not designed with considerations to fairness.
Minimizing Localized Ratio Cut Objectives in Hypergraphs
Authors: Nate Veldt, Austin R. Benson, Jon M. Kleinberg
Venue: KDD
Year: 2020

Hypergraphs are a useful abstraction for modeling multiway relationships in data, and hypergraph clustering is the task of detecting groups of closely related nodes in such data.Graph clustering has been studied extensively, and there are numerous methods for detecting small, localized clusters without having to explore an entire input graph. However, there are only a few specialized approaches for localized clustering in hypergraphs. Here we present a framework for local hypergraph clustering based on minimizing localized ratio cut objectives. Our framework takes an input set of reference nodes in a hypergraph and solves a sequence of hypergraph minimum s-t cut problems in order to identify a nearby well-connected cluster of nodes that overlaps substantially with the input set.
Parameterized Correlation Clustering in Hypergraphs and Bipartite Graphs
Authors: Nate Veldt, Anthony Wirth, David F. Gleich
Venue: KDD
Year: 2020

Motivated by applications in community detection and dense subgraph discovery, we consider new clustering objectives in hypergraphs and bipartite graphs. These objectives are parameterized by one or more resolution parameters in order to enable diverse knowledge discovery in complex data.
AI for Intelligent Financial Services: Examples and Discussion
Authors: Manuela Veloso
Venue: KDD
Year: 2020

There are many opportunities to pursue AI and ML in the financial domain. In this talk, I will overview several research directions we are pursuing in engagement with the lines of business, ranging from data and knowledge, learning from experience, reasoning and planning, multi agent systems, and secure and private AI. I will offer concrete examples of projects, and conclude with the many challenges and opportunities that AI can offer in the financial domain.
Keynote Speaker: Alessandro Vespignani
Authors: Alessandro Vespignani
Venue: KDD
Year: 2020

Alessandro Vespignani research activity is focused on the study of "techno-social" systems, where infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. In this context we aim at understanding how the very same elements assembled in large number can give rise - according to the various forces and elements at play - to different macroscopic and dynamical behaviors, opening the path to quantitative computational approaches and forecasting power. The main research lines pursued at the moment are: Develop analytical and computational models for the co-evolution and interdependence of large-scale social, technological and biological networks. Modeling contagion processes in structured populations. Developing predictive computational tools for the analysis of the spatial spread of emerging diseases. Analyze the dynamics and evolution of information and social networks. Model the adaptive behavior of social systems. Prof. Vespignani is a joint appointment between the College of Science, the College of Computer and Information Science, and the Bouvé College of Health Sciences.
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors
Authors: Daheng Wang, Meng Jiang 0001, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla
Venue: KDD
Year: 2020

User behavior modeling is important for industrial applications such as demographic attribute prediction, content recommendation, and target advertising. Existing methods represent behavior log as a sequence of adopted items and find sequential patterns; however, concrete location and time information in the behavior log, reflecting dynamic and periodic patterns, joint with the spatial dimension, can be useful for modeling users and predicting their characteristics. In this work, we propose a novel model based on graph neural networks for learning user representations from spatiotemporal behavior data. Our model's architecture incorporates two networked structures. One is a tripartite network of items, sessions, and locations. The other is a hierarchical calendar network of hour, week, and weekday nodes. It first aggregates embeddings of location and items into session embeddings via the tripartite network, and then generates user embeddings from the session embeddings via the calendar structure. The user embeddings preserve spatial patterns and temporal patterns of a variety of periodicity (e.g., hourly, weekly, and weekday patterns). It adopts the attention mechanism to model complex interactions among the multiple patterns in user behaviors. Experiments on real datasets (i.e., clicks on news articles in a mobile app) show our approach outperforms strong baselines for predicting missing demographic attributes.
Recent Advances on Graph Analytics and Its Applications in Healthcare
Authors: Fei Wang 0001, Peng Cui 0001, Jian Pei 0001, Yangqiu Song, Chengxi Zang
Venue: KDD
Year: 2020

Graph is a natural representation encoding both the features of the data samples and relationships among them. Analysis with graphs is a classic topic in data mining and many techniques have been proposed in the past. In recent years, because of the rapid development of data mining and knowledge discovery, many novel graph analytics algorithms have been proposed and successfully applied in a variety of areas. The goal of this tutorial is to summarize the graph analytics algorithms developed recently and how they have been applied in healthcare. In particular, our tutorial will cover both the technical advances and the application in healthcare. On the technical aspect, we will introduce deep network embedding techniques, graph neural networks, knowledge graph construction and inference, graph generative models and graph neural ordinary differential equation models. On the healthcare side, we will introduce how these methods can be applied in predictive modeling of clinical risks (e.g., chronic disease onset, in-hospital mortality, condition exacerbation, etc.) and disease subtyping with multi-modal patient data (e.g., electronic health records, medical image and multi-omics), knowledge discovery from biomedical literature and integration with data-driven models, as well as pharmaceutical research and development (e.g., de-novo chemical compound design and optimization, patient similarity for clinical trial recruitment and pharmacovigilance). We will conclude the whole tutorial with a set of potential issues and challenges such as interpretability, fairness and security. In particular, considering the global pandemic of COVID-19, we will also summarize the existing research that have already leveraged graph analytics to help with the understanding the mechanism, transmission, treatment and prevention of COVID-19, as well as point out the available resources and potential opportunities for future research.
Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams
Authors: Pengyang Wang, Kunpeng Liu 0001, Lu Jiang 0007, Xiaolin Li, Yanjie Fu
Venue: KDD
Year: 2020

We study the integration of reinforcement learning and spatial knowledge graph for incremental mobile user profiling, which aims to map mobile users to dynamically-updated profile vectors by incremental learning from a mixed-user event stream. After exploring many profiling methods, we identify a new imitation based criteria to better evaluate and optimize profiling accuracy. Considering the objective of teaching an autonomous agent to imitate a mobile user to plan next-visit based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the activity patterns of the user. We propose to formulate the problem into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of environment is a fused representation of a user and spatial entities (e.g., POIs, activity types, functional zones). An event that a user takes an action to visit a POI, will change the environment, resulting into a new state of user profiles and spatial entities, which helps the agent to predict next visit more accurately. After analyzing such interactions among events, users, and spatial entities, we identify (1)semantic connectivity among spatial entities, and, thus, introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected locations, activities, and zones. Besides, we identify (2) mutual influence between users and the spatial KG, and, thus, develop a mutual-updating strategy between users and the spatial KG, mixed with temporal context, to quantify the state representation that evolves over time. Along these lines, we develop a reinforcement learning framework integrated with spatial KG. The proposed framework can achieve incremental learning in multi-user profiling given a mixed-user event stream. Finally, we apply our approach to human mobility activity prediction and present extensive experiments to demonstrate improved performances.
Deep Learning for Anomaly Detection
Authors: Ruoying Wang, Kexin Nie, Yen-Jung Chang, Xinwei Gong, Tie Wang, Yang Yang 0095, Bo Long
Venue: KDD
Year: 2020

Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires researchers and developers to learn complex structure from noisy data, identify dynamic anomaly patterns, and detect anomalies with limited labels. Recent advancements in deep learning techniques have greatly improved anomaly detection performance, in comparison with classical approaches, and have extended anomaly detection to a wide variety of applications. This tutorial will help the audience gain a comprehensive understanding of deep learning based anomaly detection techniques in various application domains. First, we give an overview of the anomaly detection problem, introducing the approaches taken before the deep model era and listing out the challenges they faced. Then we survey the state-of-the-art deep learning models that range from building block neural network structures such as MLP, CNN, and LSTM, to more complex structures such as autoencoder, generative models (VAE, GAN, Flow-based models), to deep one-class detection models, etc. In addition, we illustrate how techniques such as transfer learning and reinforcement learning can help amend the label sparsity issue in anomaly detection problems and how to collect and make the best use of user labels in practice. Second to last, we discuss real world use cases coming from and outside LinkedIn. The tutorial concludes with a discussion of future trends.
Computer Vision: Deep Dive into Object Segmentation Approaches
Authors: Yuanbo Wang 0001, Osama Sakhi, Ala Eddine Ayadi, Matthew S. Hagen, Estelle Afshar
Venue: KDD
Year: 2020

Image segmentation is the task of associating pixels in an image with their respective object class labels. It has a wide range of applications in many industries including healthcare, transportation, robotics, fashion, home improvement, and tourism. Many deep learning-based approaches have been developed for image-level object recognition and pixel-level scene understanding - with the latter requiring a much denser annotation of scenes with a large set of objects. This tutorial provides an end-to-end pipeline for performing image segmentation using the state-of-art deep learning approaches and public datasets. The hands-on session will provide instructions for dataset customization, transformation, and training, validating, and testing segmentation models. The goal of this tutorial is to provide participants with a strong understanding of building image segmentation models for downstream applications.
NodeAug: Semi-Supervised Node Classification with Data Augmentation
Authors: Yiwei Wang 0001, Wei Wang 0059, Yuxuan Liang 0002, Yujun Cai, Juncheng Liu, Bryan Hooi
Venue: KDD
Year: 2020

By using Data Augmentation (DA), we present a new method to enhance Graph Convolutional Networks (GCNs), that are the state-of-the-art models for semi-supervised node classification. DA for graph data remains under-explored. Due to the connections built by edges, DA for different nodes influence each other and lead to undesired results, such as uncontrollable DA magnitudes and changes of ground-truth labels. To address this issue, we present the NodeAug (Node-Parallel Augmentation) scheme, that creates a 'parallel universe' for each node to conduct DA, to block the undesired effects from other nodes. NodeAug regularizes the model prediction of every node (including unlabeled) to be invariant with respect to changes induced by Data Augmentation (DA), so as to improve the effectiveness. To augment the input features from different aspects, we propose three DA strategies by modifying both node attributes and the graph structure. In addition, we introduce the subgraph mini-batch training for the efficient implementation of NodeAug. The approach takes the subgraph corresponding to the receptive fields of a batch of nodes as the input per iteration, rather than the whole graph that the prior full-batch training takes. Empirically, NodeAug yields significant gains for strong GCN models on the Cora, Citeseer, Pubmed, and two co-authorship networks, with a more efficient training process thanks to the proposed subgraph mini-batch training approach.
Truth Discovery against Strategic Sybil Attack in Crowdsourcing
Authors: Yue Wang 0065, Ke Wang 0001, Chunyan Miao
Venue: KDD
Year: 2020

Crowdsourcing is an information system for recruiting online workers to perform human intelligent tasks (HITs) that are hard for computers. Due to the openness of crowdsourcing, dynamic online workers with different knowledge backgrounds might give conflicting labels to a task. With the assumption that workers provide their labels independently, most existing works aggregate worker labels in a voting manner, which is vulnerable to Sybil attack where the attacker earns easy rewards by coordinating several Sybil workers to share a randomized label on each task for dominating the aggregation result. A strategic Sybil attacker also attempts to evade Sybil detection. In this paper, we propose a novel approach, called TDSSA (Truth Discovery against Strategic Sybil Attack), to defend against strategic Sybil attack. Experimental results on real-world and synthetic datasets indicate that TDSSA ensures more accurate inference of true labels under various Sybil attacking scenarios, as compared to state-of-the-art methods.
Learning to Extract Attribute Value from Product via Question Answering: A Multi-task Approach
Authors: Qifan Wang 0001, Li Yang, Bhargav Kanagal, Sumit Sanghai, D. Sivakumar 0001, Bin Shu, Zac Yu, Jon Elsas
Venue: KDD
Year: 2020

Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. It is an important research topic which has been widely studied in e-Commerce and relation learning. There are two main limitations in existing attribute value extraction methods: scalability and generalizability. Most existing methods treat each attribute independently and build separate models for each of them, which are not suitable for large scale attribute systems in real-world applications. Moreover, very limited research has focused on generalizing extraction to new attributes.
Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments
Authors: Zenan Wang, Xuan Yin, Tianbo Li, Liangjie Hong
Venue: KDD
Year: 2020

It is common in the internet industry to use offline-developed algorithms to power online products that contribute to the success of a business. Offline-developed algorithms are guided by offline evaluation metrics, which are often different from online business key performance indicators (KPIs). To maximize business KPIs, it is important to pick a north star among all available offline evaluation metrics. By noting that online products can be measured by online evaluation metrics, the online counterparts of offline evaluation metrics, we decompose the problem into two parts. As the offline A/B test literature works out the first part: counterfactual estimators of offline evaluation metrics that move the same way as their online counterparts, we focus on the second part: causal effects of online evaluation metrics on business KPIs. The north star of offline evaluation metrics should be the one whose online counterpart causes the most significant lift in the business KPI. We model the online evaluation metric as a mediator and formalize its causality with the business KPI as dose-response function (DRF). Our novel approach, causal meta-mediation analysis, leverages summary statistics of many existing randomized experiments to identify, estimate, and test the mediator DRF. It is easy to implement and to scale up, and has many advantages over the literature of mediation analysis and meta-analysis. We demonstrate its effectiveness by simulation and implementation on real data.
Multimodal Learning with Incomplete Modalities by Knowledge Distillation
Authors: Qi Wang, Liang Zhan, Paul M. Thompson, Jiayu Zhou
Venue: KDD
Year: 2020

Multimodal learning aims at utilizing information from a variety of data modalities to improve the generalization performance. One common approach is to seek the common information that is shared among different modalities for learning, whereas we can also fuse the supplementary information to leverage modality-specific information. Though the supplementary information is often desired, most existing multimodal approaches can only learn from samples with complete modalities, which wastes a considerable amount of data collected. Otherwise, model-based imputation needs to be used to complete the missing values and yet may introduce undesired noise, especially when the sample size is limited. In this paper, we proposed a framework based on knowledge distillation, utilizing the supplementary information from all modalities, and avoiding imputation and noise associated with it. Specifically, we first train models on each modality independently using all the available data. Then the trained models are used as teachers to teach the student model, which is trained with the samples having complete modalities. We demonstrate the effectiveness of the proposed method in extensive empirical studies on both synthetic datasets and real-world datasets.
City Metro Network Expansion with Reinforcement Learning
Authors: Yu Wei, Minjia Mao, Xi Zhao 0001, Jianhua Zou, Ping An
Venue: KDD
Year: 2020

City metro network expansion, included in the transportation network design, aims to design new lines based on the existing metro network. Existing methods in the field of transportation network design either (i) can hardly formulate this problem efficiently, (ii) depend on expert guidance to produce solutions, or (iii) appeal to problem-specific heuristics which are difficult to design. To address these limitations, we propose a reinforcement learning based method for the city metro network expansion problem. In this method, we formulate the metro line expansion as a Markov decision process (MDP), which characterizes the problem as a process of sequential station selection. Then, we train an actor-critic model to design the next metro line on the basis of the existing metro network. The actor is an encoder-decoder network with an attention mechanism to generate the parameterized policy which is used to select the stations. The critic estimates the expected cumulative reward to assist the training of the actor by reducing training variance. The proposed method does not require expert guidance during design, since the learning procedure only relies on the reward calculation to tune the policy for better station selection. Also, it avoids the difficulty of heuristics designing by the policy formalizing the station selection. Considering origin-destination (OD) trips and social equity, we expand the current metro network in Xi'an, China, based on the real mobility information of 24,770,715 mobile phone users in the whole city. The results demonstrate the advantages of our method compared with existing approaches.
HOPS: Probabilistic Subtree Mining for Small and Large Graphs
Authors: Pascal Welke, Florian Seiffarth, Michael Kamp, Stefan Wrobel
Venue: KDD
Year: 2020

Frequent subgraph mining, i.e., the identification of relevant patterns in graph databases, is a well-known data mining problem with high practical relevance, since next to summarizing the data, the resulting patterns can also be used to define powerful domain-specific similarity functions for prediction. In recent years, significant progress has been made towards subgraph mining algorithms that scale to complex graphs by focusing on tree patterns and probabilistically allowing a small amount of incompleteness in the result. Nonetheless, the complexity of the pattern matching component used for deciding subtree isomorphism on arbitrary graphs has significantly limited the scalability of existing approaches. In this paper, we adapt sampling techniques from mathematical combinatorics to the problem of probabilistic subtree mining in arbitrary databases of many small to medium-size graphs or a single large graph. By restricting on tree patterns, we provide an algorithm that approximately counts or decides subtree isomorphism for arbitrary transaction graphs in sub-linear time with one-sided error. Our empirical evaluation on a range of benchmark graph datasets shows that the novel algorithm substantially outperforms state-of-the-art approaches both in the task of approximate counting of embeddings in single large graphs and in probabilistic frequent subtree mining in large databases of small to medium sized graphs.