OpenRaiser/Intern-Atlas huggingface dataset:
PatMat: A Distributed Pattern Matching Engine with Cypher
Authors: Kongzhang Hao, Zhengyi Yang, Longbin Lai, Zhengmin Lai, Xin Jin, Xuemin Lin
Venue: CIKM
Year: 2019

Graph pattern matching is one of the most fundamental problems in graph database and is associated with a wide spectrum of applications. Due to its computational intensiveness, researchers have primarily devoted their efforts to improving the performance of the algorithm while constraining the graphs to have singular labels on vertices (edges) or no label. Whereas in practice graphs are typically associated with rich properties, thus the main focus in the industry is instead on powerful query languages that can express a sufficient number of pattern matching scenarios. We demo PatMat in this work to glue together the academic efforts on performance and the industrial efforts on expressiveness. To do so, we leverage the state-of-the-art join-based algorithms in the distributed contexts and Cypher query language - the most widely-adopted declarative language for graph pattern matching. The experiments demonstrate how we are capable of turning complex Cypher semantics into a distributed solution with high performance.
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
Authors: Yu He, Yangqiu Song, Jianxin Li, Cheng Ji, Jian Peng, Hao Peng
Venue: CIKM
Year: 2019

Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attention to the higher-order Markov chain nature of meta-path guided random walks, especially to the stationarity issue. In this paper, we systematically formalize the meta-path guided random walk as a higher-order Markov chain process,and present a heterogeneous personalized spacey random walk to efficiently and effectively attain the expected stationary distribution among nodes. Then we propose a generalized scalable framework to leverage the heterogeneous personalized spacey random walk to learn embeddings for multiple types of nodes in an HIN guided by a meta-path, a meta-graph, and a meta-schema respectively. We conduct extensive experiments in several heterogeneous networks and demonstrate that our methods substantially outperform the existing state-of-the-art network embedding algorithms.
Datalog Reasoning over Compressed RDF Knowledge Bases
Authors: Pan Hu, Jacopo Urbani, Boris Motik, Ian Horrocks
Venue: CIKM
Year: 2019

Materialisation is often used in RDF systems as a preprocessing step to derive all facts implied by given RDF triples and rules. Although widely used, materialisation considers all possible rule applications and can use a lot of memory for storing the derived facts, which can hinder performance. We present a novel materialisation technique that compresses the RDF triples so that the rules can sometimes be applied to multiple facts at once, and the derived facts can be represented using structure sharing. Our technique can thus require less space, as well as skip certain rule applications. Our experiments show that our technique can be very effective: when the rules are relatively simple, our system is both faster and requires less memory than prominent state-of-the-art RDF systems.
Hierarchical Multi-label Text Classification: An Attention-based Recurrent Network Approach
Authors: Wei Huang, Enhong Chen, Qi Liu, Yuying Chen, Zai Huang, Yang Liu, Zhou Zhao, Dan Zhang, Shijin Wang
Venue: CIKM
Year: 2019

Hierarchical multi-label text classification (HMTC) is a fundamental but challenging task of numerous applications (e.g., patent annotation), where documents are assigned to multiple categories stored in a hierarchical structure. Categories at different levels of a document tend to have dependencies. However, the majority of prior studies for the HMTC task employ classifiers to either deal with all categories simultaneously or decompose the original problem into a set of flat multi-label classification subproblems, ignoring the associations between texts and the hierarchical structure and the dependencies among different levels of the hierarchical structure. To that end, in this paper, we propose a novel framework called Hierarchical Attention-based Recurrent Neural Network (HARNN) for classifying documents into the most relevant categories level by level via integrating texts and the hierarchical category structure. Specifically, we first apply a documentation representing layer for obtaining the representation of texts and the hierarchical structure. Then, we develop an hierarchical attention-based recurrent layer to model the dependencies among different levels of the hierarchical structure in a top-down fashion. Here, a hierarchical attention strategy is proposed to capture the associations between texts and the hierarchical structure. Finally, we design a hybrid method which is capable of predicting the categories of each level while classifying all categories in the entire hierarchical structure precisely. Extensive experimental results on two real-world datasets demonstrate the effectiveness and explanatory power of HARNN.
Similarity-Aware Network Embedding with Self-Paced Learning
Authors: Chao Huang, Baoxu Shi, Xuchao Zhang, Xian Wu, Nitesh V. Chawla
Venue: CIKM
Year: 2019

Network embedding, which aims to learn low-dimensional vector representations for nodes in a network, has shown promising performance for many real-world applications, such as node classification and clustering. While various embedding methods have been developed for network data, they are limited in their assumption that nodes are correlated with their neighboring nodes with the same similarity degree. As such, these methods can be suboptimal for embedding network data. In this paper, we propose a new method named SANE, short for Similarity-Aware Network Embedding, to learn node representations by explicitly considering different similarity degrees between connected nodes in a network. In particular, we develop a new framework based on self-paced learning by accounting for both the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved node similarities) in network representation learning. To justify our proposed model, we perform experiments on two real-world network data. Experiments results show that SNAE outperforms state-of-the-art embedding models on the tasks of node classification and node clustering.
DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting
Authors: Siteng Huang, Donglin Wang, Xuehan Wu, Ao Tang
Venue: CIKM
Year: 2019

Multivariate time series forecasting has attracted wide attention in areas, such as system, traffic, and finance. The difficulty of the task lies in that traditional methods fail to capture complicated non-linear dependencies between time steps and between multiple time series. Recently, recurrent neural network and attention mechanism have been used to model periodic temporal patterns across multiple time steps. However, these models fit not well for time series with dynamic-period patterns or nonperiodic patterns. In this paper, we propose a dual self-attention network (DSANet) for highly efficient multivariate time series forecasting, especially for dynamic-period or nonperiodic series. DSANet completely dispenses with recurrence and utilizes two parallel convolutional components, called global temporal convolution and local temporal convolution, to capture complex mixtures of global and local temporal patterns. Moreover, DSANet employs a self-attention module to model dependencies between multiple series. To further improve the robustness, DSANet also integrates a traditional autoregressive linear model in parallel to the non-linear neural network. Experiments on real-world multivariate time series data show that the proposed model is effective and outperforms baselines.
Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity
Authors: Chao Huang, Xian Wu, Xuchao Zhang, Suwen Lin, Nitesh V. Chawla
Venue: CIKM
Year: 2019

With the increase of temporal data availability, time series classification has drawn a lot of attention in the literature because of its wide spectrum of applications in diverse domains (e.g., healthcare, bioinformatics and finance), ranging from human activity recognition to financial pattern identification. While significant progress has been made to solve time series classification problem, the success of such methods relies on data sufficiency, and may not well capture the quality embeddings when training triple instances are scarce and highly imbalance across classes. To address these challenges, we propose a prototype embedding framework-Deep Prototypical Networks (DPN), which leverages a main embedding space to capture the discrepancies of difference time series classes for alleviating data scarcity. In addition, we further augment DPN framework with a relationship-dependent masking module to automatically fuse relevant information with a distance metric learning process, which addresses the data imbalance issue and performs robust time series classification. Experimental results show significant and consistent improvements compared to state-of-the-art techniques.
System Deterioration Detection and Root Cause Learning on Time Series Graphs
Authors: Hao Huang, Shinjae Yoo, Yunwen Xu
Venue: CIKM
Year: 2019

System deterioration detection and root cause analysis is crucial for today's industrial society. However, the design and operation of mechanic system is getting more and more complex, which makes it hard at identifying deterioration with noisy data. Our research focuses on solving such problem on time-evolving sensor graphs in a streaming setting. Given a sequence of graphs, the ability to identify 1) any gradual and stable structured change and 2) the root cause components is of importance for early warning and system diagnosis. Existing methods either raise too many false alerts on instant changes or are too sensitive to noise. To address these problems, we propose Robust Failure Detection and Diagnosis (RoFaD). RoFaD can capture failure propagation given a time series of graph. By optimizing a matrix-based Taylor expansion, RoFaD can identify system deterioration in the presence of noise and immediate changes, and diagnose the root cause components. Experiments on both synthetic and real world datasets demonstrate that RoFaD is more effective than the popular baselines.
Deep Dynamic Fusion Network for Traffic Accident Forecasting
Authors: Chao Huang, Chuxu Zhang, Peng Dai, Liefeng Bo
Venue: CIKM
Year: 2019

Traffic accident forecasting is a vital part of intelligent transportation systems in urban sensing. However, predicting traffic accidents is not trivial because of two key challenges: i) the complexities of external factors which are presented with heterogeneous data structures; ii) the complex sequential transition regularities exhibited with time-dependent and high-order inter-correlations. To address these challenges, we develop a deep Dynamic Fusion Network framework (DFN), to explore the central theme of improving the ability of deep neural network on modeling heterogeneous external factors in a fully dynamic manner for traffic accident forecasting. Specifically, DFN first develops an integrative architecture, i.e., with the cooperation of a context-aware embedding module and a hierarchical fusion network, to effectively transferring knowledge from different external units for spatial-temporal pattern learning across space and time. After that, we further develop a temporal aggregation neural network layer to automatically capture relevance scores from the temporal dimension. Through extensive experiments on real-world data collected from New York City, we validate the effectiveness of our framework against various competitive methods. Besides, we also provide a qualitative analysis on prediction results to show the model interpretability.
A Benchmark for Fact Checking Algorithms Built on Knowledge Bases
Authors: Viet-Phi Huynh, Paolo Papotti
Venue: CIKM
Year: 2019

Fact checking is the task of determining if a given claim holds. Several algorithms have been developed to check claims with reference information in the form of facts in a knowledge base. While individual algorithms have been experimentally evaluated in the past, we provide the first comprehensive and publicly available benchmark infrastructure for evaluating methods across a wide range of assumptions about the claims and the reference information. We show how, by changing the popularity, transparency, homogeneity, and functionality properties of the facts in an experiment, it is possible to influence significantly the performance of the fact checking algorithms. We introduce a benchmark framework to systematically enforce such properties in training and testing datasets with fine tune control over their properties. We then use our benchmark to compare fact checking algorithms with one another, as well as with methods that can solve the link prediction task in knowledge bases. Our evaluation shows the impact of the four data properties on the qualitative performance of the fact checking solutions and reveals a number of new insights concerning their applicability and performance.
Time-Series Aware Precision and Recall for Anomaly Detection: Considering Variety of Detection Result and Addressing Ambiguous Labeling
Authors: Won-Seok Hwang, Jeong-Han Yun, Jonguk Kim, HyoungChun Kim
Venue: CIKM
Year: 2019

We proposetime-series aware precision andrecall, which are appropriate for evaluating anomaly detection methods in time-series data. In time-series data, an anomaly corresponds toa series of instances. The conventional metrics, however, overlook this characteristic, so they suffer from a problem of giving a high score to the method that only detects a long anomaly. To overcome the problem, our metrics consider thevariety of the detected anomalies to be more important through two scoring strategies,detection scoring (\ie, how many anomalies are detected) andportion scoring (\ie, how precisely each anomaly is detected). Moreover, our metrics concernambiguous instances, which indicate the instances labeled as 'normal' although they are affected by their precedent anomaly. Our metrics give smaller scores to those instances as they are likely to be anomalous. We demonstrate that our metrics are more suitable for time-series data compared to existing metrics by evaluations using a real-world dataset as well as several examples.\footnoteOur code and the detection results are available at: https://github.com/saurf4ng/TaPR.
A Semantics Aware Random Forest for Text Classification
Authors: Md Zahidul Islam, Jixue Liu, Jiuyong Li, Lin Liu, Wei Kang
Venue: CIKM
Year: 2019

The Random Forest (RF) classifiers are suitable for dealing with the high dimensional noisy data in text classification. An RF model comprises a set of decision trees each of which is trained using random subsets of features. Given an instance, the prediction by the RF is obtained via majority voting of the predictions of all the trees in the forest. However, different test instances would have different values for the features used in the trees and the trees should contribute differently to the predictions. This diverse contribution of the trees is not considered in traditional RFs. Many approaches have been proposed to model the diverse contributions by selecting a subset of trees for each instance. This paper is among these approaches. It proposes a Semantics Aware Random Forest (SARF) classifier. SARF extracts the features used by trees to generate the predictions and selects a subset of the predictions for which the features are relevant to the predicted classes. We evaluated SARF's classification performance on $30$ real-world text datasets and assessed its competitiveness with state-of-the-art ensemble selection methods. The results demonstrate the superior performance of the proposed approach in textual information retrieval and initiate a new direction of research to utilise interpretability of classifiers.
NActSeer: Predicting User Actions in Social Network using Graph Augmented Neural Network
Authors: Mohammad Raihanul Islam, Sathappan Muthiah, Naren Ramakrishnan
Venue: CIKM
Year: 2019

Nowadays social network platforms like Twitter, Facebook, Weibo have created a new landscape to communicate with our friends and the world at large. In this landscape our social activities, purchase decisions, check-ins etc. become available immediately to our friends/followers and thus encouraging them to involve in the same activity. This gives rise to the question, given a user and her friends' previous actions, can we predict what is she going to do next? This problem can serve as a good indicator enabling policy research, targeted advertising, assortment planning etc. To capture such sequential mechanism two broad classes of methods have been proposed in the past. First one is the Markov Chain (MC), which assumes user's next action can be predicted based on her most recently taken actions while the second type of approach i.e. Recurrent Neural Network (RNN) tries to model both long and short term preferences of a user. However, none of the two classes of models contain any integrated mechanism to capture the preferences of neighbor's actions. To fill this gap, we propose a social network augmented neural network model named NActSeer which takes the neighbors' actions into account in addition to the user's history. To achieve this NActSeer maintains a dynamic user embedding based on the activities within a time window. It then learns a feature representation for each user which is augmented by her neighbors. Empirical studies on four real-world datasets show that NActSeer is able to outperform several classical and state-of-the-art models proposed for similar problems and achieves up to 71% performance boost.
Geolocating Tweets in any Language at any Location
Authors: Mike Izbicki, Vagelis Papalexakis, Vassilis J. Tsotras
Venue: CIKM
Year: 2019

Most social media messages are written in languages other than English, but commonly used text mining tools were designed only for English. This paper introduces the Unicode Convolutional Neural Network (UnicodeCNN) for analyzing text written in any language. The UnicodeCNN does not require the language to be known in advance, allows the language to change arbitrarily mid-sentence, and is robust to the misspellings and grammatical mistakes commonly found in social media. We demonstrate the UnicodeCNN's effectiveness on the challenging task of content-based tweet geolocation using a dataset with 900 million tweets written in more than 100 languages. Whereas previous work restricted itself to predicting a tweet's country or city of origin (and only worked on tweets written in certain languages from highly populated cities), we predict the exact GPS locations of tweets (and our method works on tweets written in any language sent from anywhere in the world). We predict GPS coordinates using the mixture of von Mises-Fisher (MvMF) distribution. The MvMF exploits the Earth's spherical geometry to improve predictions, a task that previous work considered too computationally difficult. On English tweets, our model's predictions average more than 300km closer to the true location than previous work, and in other languages our model's predictions are up to 1500km more accurate. Remarkably, the UnicodeCNN can learn geographic knowledge in one language and automatically transfer that knowledge to other languages.
Document in Context of its Time (DICT): Providing Temporal Context to Support Analysis of Past Documents
Authors: Adam Jatowt, Ricardo Campos, Sourav S. Bhowmick, Antoine Doucet
Venue: CIKM
Year: 2019

Old documents tend to be difficult to be analyzed and understood, not only for average users but oftentimes for professionals as well. This is due to the context shift, vocabulary evolution and, in general, the lack of precise knowledge about the writing styles in the past. We propose a concept of positioning document in the context of its time, and develop an interactive system to support such an objective. Our system helps users to know whether the vocabulary used by an author in the past were frequent at the time of text creation, whether the author used anachronisms or neologisms, and so on. It also enables detecting terms in text that underwent considerable semantic change and provides more information on the nature of such change. Overall, the proposed tool offers additional knowledge on the writing style and vocabulary choice in documents by drawing from data collected at the time of their creation or at other user-specified time.
Active Collaborative Sensing for Energy Breakdown
Authors: Yiling Jia, Nipun Batra, Hongning Wang, Kamin Whitehouse
Venue: CIKM
Year: 2019

Residential homes constitute roughly one-fourth of the total energy usage worldwide. Providing appliance-level energy breakdown has been shown to induce positive behavioral changes that can reduce energy consumption by 15%. Existing approaches for energy breakdown either require hardware installation in every target home or demand a large set of energy sensor data available for model training. However, very few homes in the world have installed sub-meters (sensors measuring individual appliance energy); and the cost of retrofitting a home with extensive sub-metering eats into the funds available for energy saving retrofits. As a result, strategically deploying sensing hardware to maximize the reconstruction accuracy of sub-metered readings in non-instrumented homes while minimizing deployment costs becomes necessary and promising. In this work, we develop an active learning solution based on low-rank tensor completion for energy breakdown. We propose to actively deploy energy sensors to appliances from selected homes, with a goal to improve the prediction accuracy of the completed tensor with minimum sensor deployment cost. We empirically evaluate our approach on the largest public energy dataset collected in Austin, Texas, USA, from 2013 to 2017. The results show that our approach gives better performance with fixed number of sensors installed, when compared to the state-of-the-art, which is also proven by our theoretical analysis.
Learning to Predict Human Stress Level with Incomplete Sensor Data from Wearable Devices
Authors: Jyun-Yu Jiang, Zehan Chao, Andrea L. Bertozzi, Wei Wang, Sean D. Young, Deanna Needell
Venue: CIKM
Year: 2019

Stress is a common problem in modern life that can bring both psychological and physical disorder. Wearable sensors are commonly used to study the relationship between physical records and mental status. Although sensor data generated by wearable devices provides an opportunity to identify stress in people for predictive medicine, in practice, the data are typically complicated and vague and also often fragmented. In this paper, we propose DataCompletion with Diurnal Regularizers (DCDR) and TemporallyHierarchical Attention Network (THAN) to address the fragmented data issue and predict human stress level with recovered sensor data. We model fragmentation as a sparsity issue. The nuclear norm minimization method based on the low-rank assumption is first applied to derive unobserved sensor data with diurnal patterns of human behaviors. A hierarchical recurrent neural network with the attention mechanism then models temporally structural information in the reconstructed sensor data, thereby inferring the predicted stress level. Data for this study were from 75 undergraduate students (taken from a sample of a larger study) who provided sensor data from smart wristbands. They also completed weekly stress surveys as ground-truth labels about their stress levels. This survey lasted 12 weeks and the sensor records are also in this period. The experimental results demonstrate that our approach significantly outperforms conventional methods in both data completion and stress level prediction. Moreover, an in-depth analysis further shows the effectiveness and robustness of our approach.