OpenRaiser/Intern-Atlas huggingface dataset:
Active Opinion Maximization in Social Networks
Authors: Xinyue Liu, Xiangnan Kong, Philip S. Yu
Venue: KDD
Year: 2018

Influence maximization (IM) targets at maximizing the number of users being aware of a product by finding a set of seed users to expose in a social network. Previous IM models mainly focus on optimizing the spread of product consumption, which assumes that all users are potential customers and more exposures lead to better profit. However, in the real-world scenario, some people may not like the product and may express negative opinions after consuming, which damage the product reputation and harm the long-term profit. Only a portion of users in the social network, called the target user, is the potential customer that likes the product and will spread positive opinion. In this paper, we consider a problem called AcTive Opinion Maximization (ATOM), where the goal is to find a set of seed users to maximize the overall opinion spread toward a target product in a multi-round campaign. Different from previous works, we do not assume the user opinion is known before consumption, but should be derived from user preference data. The ATOM problem has essential applications in viral marketing, such as reputation building and precision advertising. Given its significance, ATOM problem is profoundly challenging due to the hardness of estimating user opinion in a multi-round campaign. Moreover, the process of opinion estimation and influence propagation intertwine with each other, which requires the model to consider the two components collectively. We propose an active learning framework called CONE (aCtive OpinioN Estimator) to address above challenges. Experimental results on two real-world datasets demonstrate that CONE improves the total opinion spread in a social network.
Where Will Dockless Shared Bikes be Stacked?: - Parking Hotspots Detection in a New City
Authors: Zhaoyang Liu, Yanyan Shen, Yanmin Zhu
Venue: KDD
Year: 2018

Dockless shared bikes, which aim at providing a more flexible and convenient solution to the first-and-last mile connection, come into China and expand to other countries at a very impressing speed. The expansion of shared bike business in new cities brings many challenges among which, the most critical one is the parking chaos caused by too many bikes yet insufficient demands. To allow possible actions to be taken in advance, this paper studies the problem of detecting parking hotspots in a new city where no dockless shared bike has been deployed. We propose to measure road hotness by bike density with the help of the Kernal Density Estimation. We extract useful features from multi-source urban data and introduce a novel domain adaption network for transferring hotspots knowledge learned from one city with shared bikes to a new city. The extensive experimental results demonstrate the effectiveness of our proposed approach compared with various baselines.
Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation
Authors: Boyang Liu, Pang-Ning Tan, Jiayu Zhou
Venue: KDD
Year: 2018

Multilevel modeling and multi-task learning are two widely used approaches for modeling nested (multi-level) data, which contain observations that can be clustered into groups, characterized by their group-level features. Despite the similarity of the problems they address, the explicit relationship between multilevel modeling and multi-task learning has not been carefully examined. In this paper, we present a comparative analysis between the two methods to illustrate their strengths and limitations when applied to two-level nested data. We provide a detailed analysis demonstrating the equivalence of their formulations under a mild condition from an optimization perspective. We also demonstrate their limitations in terms of their predictive performance and especially, their difficulty in identifying potential cross-scale interactions between the local and group-level features when applied to datasets with either a small number of groups or limited training examples per group. To overcome these limitations, we propose a novel method for disaggregating the coarse-scale values of the group-level features in the nested data. Experimental results on both synthetic and real-world data show that the disaggregated group-level features can help enhance the prediction accuracy of the models significantly and identify the cross-scale interactions more effectively.
Context-aware Academic Collaborator Recommendation
Authors: Zheng Liu, Xing Xie, Lei Chen
Venue: KDD
Year: 2018

Collaborator Recommendation is a useful application in exploiting big academic data. However, existing works leave out the contextual restriction (i.e., research topics) of people's academic collaboration, thus cannot recommend suitable collaborators for the required research topics. In this work, we propose Context-aware Collaborator Recommendation (CACR), which aims to recommend high-potential new collaborators for people's context-restricted requests. To this end, we design a novel recommendation framework, which consists of two fundamental components: the Collaborative Entity Embedding network (CEE) and the Hierarchical Factorization Model (HFM). In particular, CEE jointly represents researchers and research topics as compact vectors based on their co-occurrence relationships, whereby capturing researchers' context-aware collaboration tendencies and topics' underlying semantics. Meanwhile, HFM extracts researchers' activenesses and conservativenesses, which reflect their intensities of making academic collaborations and tendencies of working with non-collaborated fellows. The extracted activenesses and conservativenesses work collaboratively with the context-aware collaboration tendencies, such that high-quality recommendation can be produced. Extensive experimental studies are conducted with large-scale academic data, whose results verify the effectiveness of our proposed approaches.
STAMP: Short-Term Attention/Memory Priority Model for Session-based Recommendation
Authors: Qiao Liu, Yifu Zeng, Refuoe Mokhosi, Haibin Zhang
Venue: KDD
Year: 2018

Predicting users' actions based on anonymous sessions is a challenging problem in web-based behavioral modeling research, mainly due to the uncertainty of user behavior and the limited information. Recent advances in recurrent neural networks have led to promising approaches to solving this problem, with long short-term memory model proving effective in capturing users' general interests from previous clicks. However, none of the existing approaches explicitly take the effects of users' current actions on their next moves into account. In this study, we argue that a long-term memory model may be insufficient for modeling long sessions that usually contain user interests drift caused by unintended clicks. A novel short-term attention/memory priority model is proposed as a remedy, which is capable of capturing users' general interests from the long-term memory of a session context, whilst taking into account users' current interests from the short-term memory of the last-clicks. The validity and efficacy of the proposed attention mechanism is extensively evaluated on three benchmark data sets from the RecSys Challenge 2015 and CIKM Cup 2016. The numerical results show that our model achieves state-of-the-art performance in all the tests.
Interactive Paths Embedding for Semantic Proximity Search on Heterogeneous Graphs
Authors: Zemin Liu, Vincent W. Zheng, Zhou Zhao, Zhao Li, Hongxia Yang, Minghui Wu, Jing Ying
Venue: KDD
Year: 2018

Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to measure the semantic proximity by paths connecting a query object and a target object. Despite the success of such path-based approaches, they often modeled the paths in a weakly coupled manner, which overlooked the rich interactions among paths. In this paper, we introduce a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object. We then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation. We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines.
TINET: Learning Invariant Networks via Knowledge Transfer
Authors: Chen Luo, Zhengzhang Chen, Lu-An Tang, Anshumali Shrivastava, Zhichun Li, Haifeng Chen, Jieping Ye
Venue: KDD
Year: 2018

The latent behavior of an information system that can exhibit extreme events, such as system faults or cyber-attacks, is complex. Recently, the invariant network has shown to be a powerful way of characterizing complex system behaviors. Structures and evolutions of the invariance network, in particular, the vanishing correlations, can shed light on identifying causal anomalies and performing system diagnosis. However, due to the dynamic and complex nature of real-world information systems, learning a reliable invariant network in a new environment often requires continuous collecting and analyzing the system surveillance data for several weeks or even months. Although the invariant networks learned from old environments have some common entities and entity relationships, these networks cannot be directly borrowed for the new environment due to the domain variety problem. To avoid the prohibitive time and resource consuming network building process, we propose TINET, a knowledge transfer based model for accelerating invariant network construction. In particular, we first propose an entity estimation model to estimate the probability of each source domain entity that can be included in the final invariant network of the target domain. Then, we propose a dependency construction model for constructing the unbiased dependency relationships by solving a two-constraint optimization problem. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness and efficiency of TINET. We also apply TINET to a real enterprise security system for intrusion detection. TINET achieves superior detection performance at least 20 days lead-lag time in advance with more than 75% accuracy.
Hierarchical Taxonomy Aware Network Embedding
Authors: Jianxin Ma, Peng Cui, Xiao Wang, Wenwu Zhu
Venue: KDD
Year: 2018

Network embedding learns the low-dimensional representations for vertices, while preserving the inter-vertex similarity reflected by the network structure. The neighborhood structure of a vertex is usually closely related with an underlying hierarchical taxonomy---the vertices are associated with successively broader categories that can be organized hierarchically. The categories of different levels reflects similarity of different granularity. The hierarchy of the taxonomy therefore requires that the learned representations support multiple levels of granularity. Moreover, the hierarchical taxonomy enables the information to flow between vertices via their common categories, and thus provides an effective mechanism for alleviating data scarcity. However, incorporating the hierarchical taxonomy into network embedding poses a great challenge (since the taxonomy is generally unknown), and it is neglected by the existing approaches. In this paper, we propose NetHiex, a NETwork embedding model that captures the latent HIErarchical taXonomy. In our model, a vertex representation consists of multiple components that are associated with categories of different granularity. The representations of both the vertices and the categories are co-regularized. We employ the nested Chinese restaurant process to guide the search of the most plausible hierarchical taxonomy. The network structure is then recovered from the latent representations via a Bernoulli distribution. The whole model is unified within a nonparametric probabilistic framework. A scalable expectation-maximization algorithm is derived for optimization. Empirical results demonstrate that NetHiex achieves significant performance gain over the state-of-arts.
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Authors: Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed H. Chi
Venue: KDD
Year: 2018

Neural-based multi-task learning has been successfully used in many real-world large-scale applications such as recommendation systems. For example, in movie recommendations, beyond providing users movies which they tend to purchase and watch, the system might also optimize for users liking the movies afterwards. With multi-task learning, we aim to build a single model that learns these multiple goals and tasks simultaneously. However, the prediction quality of commonly used multi-task models is often sensitive to the relationships between tasks. It is therefore important to study the modeling tradeoffs between task-specific objectives and inter-task relationships. In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data. We adapt the Mixture-of-Experts (MoE) structure to multi-task learning by sharing the expert submodels across all tasks, while also having a gating network trained to optimize each task. To validate our approach on data with different levels of task relatedness, we first apply it to a synthetic dataset where we control the task relatedness. We show that the proposed approach performs better than baseline methods when the tasks are less related. We also show that the MMoE structure results in an additional trainability benefit, depending on different levels of randomness in the training data and model initialization. Furthermore, we demonstrate the performance improvements by MMoE on real tasks including a binary classification benchmark, and a large-scale content recommendation system at Google.
xStream: Outlier Detection in Feature-Evolving Data Streams
Authors: Emaad A. Manzoor, Hemank Lamba, Leman Akoglu
Venue: KDD
Year: 2018

This work addresses the outlier detection problem for feature-evolving streams, which has not been studied before. In this setting both (1) data points may evolve, with feature values changing, as well as (2) feature space may evolve, with newly-emerging features over time. This is notably different from row-streams, where points with fixed features arrive one at a time. We propose a density-based ensemble outlier detector, called xStream, for this more extreme streaming setting which has the following key properties: (1) it is a constant-space and constant-time (per incoming update) algorithm, (2) it measures outlierness at multiple scales or granularities, it can handle (3 i ) high-dimensionality through distance-preserving projections, and (3$ii$) non-stationarity via $O(1)$-time model updates as the stream progresses. In addition, xStream can address the outlier detection problem for the (less general) disk-resident static as well as row-streaming settings. We evaluate xStream rigorously on numerous real-life datasets in all three settings: static, row-stream, and feature-evolving stream. Experiments under static and row-streaming scenarios show that xStream is as competitive as state-of-the-art detectors and particularly effective in high-dimensions with noise. We also demonstrate that our solution is fast and accurate with modest space overhead for evolving streams, on which there exists no competition.
DILOF: Effective and Memory Efficient Local Outlier Detection in Data Streams
Authors: Gyoung S. Na, Donghyun Kim, Hwanjo Yu
Venue: KDD
Year: 2018

With precipitously growing demand to detect outliers in data streams, many studies have been conducted aiming to develop extensions of well-known outlier detection algorithm called Local Outlier Factor (LOF), for data streams. However, existing LOF-based algorithms for data streams still suffer from two inherent limitations: 1) Large amount of memory space is required. 2) A long sequence of outliers is not detected. In this paper, we propose a new outlier detection algorithm for data streams, called DILOF that effectively overcomes the limitations. To this end, we first develop a novel density-based sampling algorithm to summarize past data and then propose a new strategy for detecting a sequence of outliers. It is worth noting that our proposing algorithms do not require any prior knowledge or assumptions on data distribution. Moreover, we accelerate the execution time of DILOF about 15 times by developing a powerful distance approximation technique. Our comprehensive experiments on real-world datasets demonstrate that DILOF significantly outperforms the state-of-the-art competitors in terms of accuracy and execution time. The source code for the proposed algorithm is available at our website: http://di.postech.ac.kr/DILOF.
Robust Bayesian Kernel Machine via Stein Variational Gradient Descent for Big Data
Authors: Khanh Nguyen, Trung Le, Tu Dinh Nguyen, Dinh Q. Phung, Geoffrey I. Webb
Venue: KDD
Year: 2018

Kernel methods are powerful supervised machine learning models for their strong generalization ability, especially on limited data to effectively generalize on unseen data. However, most kernel methods, including the state-of-the-art LIBSVM, are vulnerable to the curse of kernelization, making them infeasible to apply to large-scale datasets. This issue is exacerbated when kernel methods are used in conjunction with a grid search to tune their kernel parameters and hyperparameters which brings in the question of model robustness when applied to real datasets. In this paper, we propose a robust Bayesian Kernel Machine (BKM) - a Bayesian kernel machine that exploits the strengths of both the Bayesian modelling and kernel methods. A key challenge for such a formulation is the need for an efficient learning algorithm. To this end, we successfully extended the recent Stein variational theory for Bayesian inference for our proposed model, resulting in fast and efficient learning and prediction algorithms. Importantly our proposed BKM is resilient to the curse of kernelization, hence making it applicable to large-scale datasets and robust to parameter tuning, avoiding the associated expense and potential pitfalls with current practice of parameter tuning. Our extensive experimental results on 12 benchmark datasets show that our BKM without tuning any parameter can achieve comparable predictive performance with the state-of-the-art LIBSVM and significantly outperforms other baselines, while obtaining significantly speedup in terms of the total training time compared with its rivals
Calibrated Multi-Task Learning
Authors: Feiping Nie, Zhanxuan Hu, Xuelong Li
Venue: KDD
Year: 2018

This paper proposes a novel algorithm, named Non-Convex Calibrated Multi-Task Learning (NC-CMTL), for learning multiple related regression tasks jointly. Instead of utilizing the nuclear norm, NC-CMTL adopts a non-convex low rank regularizer to explore the shared information among different tasks. In addition, considering that the regularization parameter for each regression task desponds on its noise level, we replace the least squares loss function by square-root loss function. Computationally, as proposed model has a nonsmooth loss function and a non-convex regularization term, we construct an efcient re-weighted method to optimize it. Theoretically, we frst present the convergence analysis of constructed method, and then prove that the derived solution is a stationary point of original problem. Particularly, the regularizer and optimization method used in this paper are also suitable for other rank minimization problems. Numerical experiments on both synthetic and real data illustrate the advantages of NC-CMTL over several state-of-the-art methods.
Multiview Clustering via Adaptively Weighted Procrustes
Authors: Feiping Nie, Lai Tian, Xuelong Li
Venue: KDD
Year: 2018

In this paper, we make a multiview extension of the spectral rotation technique raised in single view spectral clustering research. Since spectral rotation is closely related to the Procrustes Analysis for points matching, we point out that classical Procrustes Average approach can be used for multiview clustering. Besides, we show that direct applying Procrustes Average (PA) in multiview tasks may not be optimal theoretically and empirically, since it does not take the clustering capacity differences of different views into consideration. Other than that, we propose an Adaptively Weighted Procrustes (AWP) approach to overcome the aforementioned deficiency. Our new AWP weights views with their clustering capacities and forms a weighted Procrustes Average problem accordingly. The optimization algorithm to solve the new model is computational complexity analyzed and convergence guaranteed. Experiments on five real-world datasets demonstrate the effectiveness and efficiency of the new models.
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Authors: Guansong Pang, Longbing Cao, Ling Chen, Huan Liu
Venue: KDD
Year: 2018

Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers). This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random distance-based approach. This customized learning yields more optimal and stable representations for the targeted outlier detectors. Additionally, RAMODO can leverage little labeled data as prior knowledge to learn more expressive and application-relevant representations. We instantiate RAMODO to an efficient method called REPEN to demonstrate the performance of RAMODO. Extensive empirical results on eight real-world ultrahigh dimensional data sets show that REPEN (i) enables a random distance-based detector to obtain significantly better AUC performance and two orders of magnitude speedup; (ii) performs substantially better and more stably than four state-of-the-art representation learning methods; and (iii) leverages less than 1% labeled data to achieve up to 32% AUC improvement.
EvoGraph: An Effective and Efficient Graph Upscaling Method for Preserving Graph Properties
Authors: Himchan Park, Min-Soo Kim
Venue: KDD
Year: 2018

Nowadays, many researchers and industry groups often suffer from the lack of a variety of large-scale real graphs. Although a lot of synthetic graph generation methods,(or models) such as RMAT and BA have been developed, their output graphs tend to be quite different from real-world graphs in terms of graph properties. There are a few graph upscaling methods such as Gscaler, they still fail to preserve important properties of the original graph and fail to upscale due to out of memory or too long runtime. In this paper, we propose a novel graph upscaling method called EvoGraph that can upscale the original graph with preserving its properties regardless of a scale factor. It determines and attaches new edges to the real graph using the preferential attachment mechanism in an effective and efficient way. Through extensive experiments, we have demonstrated that EvoGraph significantly outperforms the state-of-the-art graph upscaling method Gscaler in terms of preserving graph properties and performance measures such as runtime, memory usage, and scalability.