OpenRaiser/Intern-Atlas huggingface dataset:
A Model-Free Approach to Infer the Diffusion Network from Event Cascade
Authors: Yu Rong, Qiankun Zhu, Hong Cheng
Venue: CIKM
Year: 2016

Information diffusion through various types of networks, such as social networks and media networks, is a very common phenomenon on the Internet nowadays. In many scenarios, we can track only the time when the information reaches a node. However, the source infecting this node is usually unobserved. Inferring the underlying diffusion network based on cascade data (observed sequence of infected nodes with timestamp) without additional information is an essential and challenging task in information diffusion. Many studies have focused on constructing complex models to infer the underlying diffusion network in a parametric way. However, the diffusion process in the real world is very complex and hard to be captured by a parametric model. Even worse, inferring the parameters of a complex model is impractical under a large data volume.
The Rich and the Poor: A Markov Decision Process Approach to Optimizing Taxi Driver Revenue Efficiency
Authors: Huigui Rong, Xun Zhou, Chang Yang, M. Zubair Shafiq, Alex X. Liu
Venue: CIKM
Year: 2016

Taxi services play an important role in the public transportation system of large cities. Improving taxi business efficiency is an important societal problem since it could improve the income of the drivers and reduce gas emissions and fuel consumption. The recent research on seeking strategies may not be optimal for the overall revenue over an extended period of time as they ignored the important impact of passengers' destinations on future passenger seeking. To address these issues, this paper investigates how to increase the revenue efficiency (revenue per unit time) of taxi drivers, and models the passenger seeking process as a Markov Decision Process (MDP). For each one-hour time slot, we learn a different set of parameters for the MDP from data and find the best move for a vacant taxi to maximize the total revenue in that time slot. A case study and several experimental evaluations on a real dataset from a major city in China show that our proposed approach improves the revenue efficiency of inexperienced drivers by up to 15% and outperforms a baseline method in all the time slots.
Leveraging Multiple GPUs and CPUs for Graphlet Counting in Large Networks
Authors: Ryan A. Rossi, Rong Zhou
Venue: CIKM
Year: 2016

Massively parallel architectures such as the GPU are becoming increasingly important due to the recent proliferation of data. In this paper, we propose a key class of hybrid parallel graphlet algorithms that leverages multiple CPUs and GPUs simultaneously for computing k-vertex induced subgraph statistics (called graphlets). In addition to the hybrid multi-core CPU-GPU framework, we also investigate single GPU methods (using multiple cores) and multi-GPU methods that leverage all available GPUs simultaneously for computing induced subgraph statistics. Both methods leverage GPU devices only, whereas the hybrid multi-core CPU-GPU framework leverages all available multi-core CPUs and multiple GPUs for computing graphlets in large networks. Compared to recent approaches, our methods are orders of magnitude faster, while also more cost effective enjoying superior performance per capita and per watt. In particular, the methods are up to 300 times faster than a recent state-of-the-art method. To the best of our knowledge, this is the first work to leverage multiple CPUs and GPUs simultaneously for computing induced subgraph statistics.
EnerQuery: Energy-Aware Query Processing
Authors: Amine Roukh, Ladjel Bellatreche, Carlos Ordonez
Venue: CIKM
Year: 2016

Energy consumption is increasingly more important in large-scale query processing. This problem requires revisiting traditional query processing in actual DBMSs to identify the potential of energy saving, and to study the trade-offs between energy consumption and performance. In this paper, we propose EnerQuery, a tool built on top of a traditional DBMS to capitalize the efforts invested in building energy-aware query optimizers, which have the lion's share in energy consumption. Energy consumption is estimated on all query plan steps and integrated into a mathematical linear cost model used to select the best query plans. To increase end users' energy awareness, EnerQuery features a diagnostic GUI to visualize energy consumption per step and its savings when tuning key parameters during query execution.
Forecasting Seasonal Time Series Using Weighted Gradient RBF Network based Autoregressive Model
Authors: Wenjie Ruan, Quan Z. Sheng, Peipei Xu, Nguyen Khoi Tran, Nickolas J. G. Falkner, Xue Li, Wei Emma Zhang
Venue: CIKM
Year: 2016

How to accurately forecast seasonal time series is very important for many business area such as marketing decision, planning production and profit estimation. In this paper, we propose a weighted gradient Radial Basis Function Network based AutoRegressive (WGRBF-AR) model for modeling and predicting the nonlinear and non-stationary seasonal time series. This WGRBF-AR model is a synthesis of the weighted gradient RBF network and the functional-coefficient autoregressive (FAR) model through using the WGRBF networks to approximate varying coefficients of FAR model. It not only takes the advantages of the FAR model in nonlinear dynamics description but also inherits the capability of the WGRBF network to deal with non-stationarity. We test our model using ten-years retail sales data on five different commodity in US. The results demonstrate that the proposed WGRBF-AR model can achieve competitive prediction accuracy compared with the state-of-the-art.
When Sensor Meets Tensor: Filling Missing Sensor Values Through a Tensor Approach
Authors: Wenjie Ruan, Peipei Xu, Quan Z. Sheng, Nguyen Khoi Tran, Nickolas J. G. Falkner, Xue Li, Wei Emma Zhang
Venue: CIKM
Year: 2016

In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive time-series sensory data with geo-tags. However, such sensory readings are easily missing due to various reasons such as the hardware malfunction, connection errors, and data corruption. This paper focuses on this challenge--how to accurately yet efficiently recover the missing values for corrupted time-series sensor data with geo-stamps. In this paper, we formulate the time-series sensor data as a 3-order tensor that naturally preserves sensors' temporal and spatial dependencies. Then we exploit its low-rank and sparse-noise structures by drawing upon recent advances in Robust Principal Component Analysis (RPCA) and tensor completion theory. The main novelty of this paper lies in that, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to recover the data tensor. Besides testing our method using the synthetic data, we also design a real-world testbed by passive RFID (RadioFrequency IDentification) sensors. The results demonstrate the effectiveness and accuracy of our approach.
Generative Feature Language Models for Mining Implicit Features from Customer Reviews
Authors: Shubhra Kanti Karmaker Santu, Parikshit Sondhi, ChengXiang Zhai
Venue: CIKM
Year: 2016

Online customer reviews are very useful for both helping consumers make buying decisions on products or services and providing business intelligence. However, it is a challenge for people to manually digest all the opinions buried in large amounts of review data, raising the need for automatic opinion summarization and analysis. One fundamental challenge in automatic opinion summarization and analysis is to mine implicit features, i.e., recognizing the features implicitly mentioned (referred to) in a review sentence. Existing approaches require many ad hoc manual parameter tuning, and are thus hard to optimize or generalize; their evaluation has only been done with Chinese review data. In this paper, we propose a new approach based on generative feature language models that can mine the implicit features more effectively through unsupervised statistical learning. The parameters are optimized automatically using an Expectation-Maximization algorithm. We also created eight new data sets to facilitate evaluation of this task in English. Experimental results show that our proposed approach is very effective for assigning features to sentences that do not explicitly mention the features, and outperforms the existing algorithms by a large margin.
Uncovering Fake Likers in Online Social Networks
Authors: Prudhvi Ratna Badri Satya, Kyumin Lee, Dongwon Lee, Thanh Tran, Jason (Jiasheng) Zhang
Venue: CIKM
Year: 2016

As the commercial implications of Likes in online social networks multiply, the number of fake Likes also increase rapidly. To maintain a healthy ecosystem, however, it is critically important to prevent and detect such fake Likes. Toward this goal, in this paper, we investigate the problem of detecting the so-called "fake likers" who frequently make fake Likes for illegitimate reasons. To uncover fake Likes in online social networks, we: (1) first collect a substantial number of profiles of both fake and legitimate Likers using linkage and honeypot approaches, (2) analyze the characteristics of both types of Likers, (3) identify effective features exploiting the learned characteristics and apply them in supervised learning models, and (4) thoroughly evaluate their performances against three baseline methods and under two attack models. Our experimental results show that our proposed methods with effective features significantly outperformed baseline methods, with accuracy = 0.871, false positive rate = 0.1, and false negative rate = 0.14.
MIST: Missing Person Intelligence Synthesis Toolkit
Authors: Elham Shaabani, Hamidreza Alvari, Paulo Shakarian, J. E. Kelly Snyder
Venue: CIKM
Year: 2016

Each day, approximately 500 missing persons cases occur that go unsolved/unresolved in the United States. The non-profit organization known as the Find Me Group (FMG), led by former law enforcement professionals, is dedicated to solving or resolving these cases. This paper introduces the Missing Person Intelligence Synthesis Toolkit (MIST) which leverages a data-driven variant of geospatial abductive inference. This system takes search locations provided by a group of experts and rank-orders them based on the probability assigned to areas based on the prior performance of the experts taken as a group. We evaluate our approach compared to the current practices employed by the Find Me Group and found it significantly reduces the search area - leading to a reduction of 31 square miles over 24 cases we examined in our experiments. Currently, we are using MIST to aid the Find Me Group in an active missing person case.
Extracting Skill Endorsements from Personal Communication Data
Authors: Darshan M. Shankaralingappa, Gianmarco De Francisci Morales, Aristides Gionis
Venue: CIKM
Year: 2016

People are increasingly communicating and collaborating via digital platforms, such as email and messaging applications. Data exchanged on these digital communication platforms can be a treasure trove of information on people who participate in the discussions: who they are collaborating with, what they are working on, what their expertise is, and so on. Yet, personal communication data is very rarely analyzed due to the sensitivity of the information it contains. In this paper, we mine personal communication data with the goal of generating skill endorsements of the type "person A endorses person B on skill X." To address privacy concerns, we consider that each person has access only to their own data (i.e., conversations with their peers). By using our method, they can generate endorsements for their peers, which they can inspect and opt to publish. To identify meaningful skills we use a knowledge base created from the StackExchange Q&A forum. We study two different approaches, one based on building a skill graph, and one based on information retrieval techniques. We find that the latter approach outperforms the graph-based algorithms when tested on a dataset of user profiles from StackOverflow. We also conduct a user study on email data from nine volunteers, and we find that the information retrieval-based approach achieves a [email protected] score of 0.617.
Digesting News Reader Comments via Fine-Grained Associations with Event Facets and News Contents
Authors: Bei Shi, Wai Lam
Venue: CIKM
Year: 2016

News articles from different sources reporting the same event are often associated with an enormous amount of reader comments resulting in difficulty in digesting the comments manually. Some of these comments, despite coming from different sources, discuss about a certain facet of the event. On the other hand, some comments discuss on the specific topic of the corresponding news article. We propose a framework that can digest reader comments automatically via fine-grained associations with event facets and news. We propose an unsupervised model called DRC, based on collective matrix factorization and develop a multiplicative-update method to infer the parameters. Experimental results show that our proposed DRC model can provide an effective way to digest news reader comments.
Digesting Multilingual Reader Comments via Latent Discussion Topics with Commonality and Specificity
Authors: Bei Shi, Wai Lam, Lidong Bing, Yinqing Xu
Venue: CIKM
Year: 2016

Many news websites from different regions in the world allow readers to write comments in their own languages about an event. Digesting such enormous amount of comments in different languages is difficult. One elegant way to digest and organize these comments is to detect latent discussion topics with the consideration of language attributes. Some discussion topics are common topics shared between languages whereas some topics are specifically dominated by a particular language. To tackle this task of discovering discussion topics that exhibit commonality or specificity from news reader comments written in different languages, we propose a new model called TDCS based on graphical models, which can cope with the language gap and detect language-common and language-specific latent discussion topics simultaneously. Our TDCS model also exploits comment-oriented clues via a scalable Dirichlet Multinomial Regression method. To learn the model parameters, we develop an inference method which alternates between EM and Gibbs sampling. Experimental results show that our proposed TDCS model can provide an effective way to digest multilingual news reader comments.
ACM DAVA'16: 2nd International Workshop on DAta mining meets Visual Analytics at Big Data Era
Authors: Lei Shi, Hanghang Tong, Chaoli Wang, Leman Akoglu
Venue: CIKM
Year: 2016

The theme of this workshop is to bridge data mining and visual analytics for information and knowledge management. The topics include, but not limited to, the following: Big data mining and visual analytics, theory and foundations -- Knowledge discovery with data mining and visual analytics technologies -- Fusion, mining and visualization of rich and heterogeneous data source -- Security and privacy issues in data mining and visual analytics systems -- Information, social and biological graph mining and visualization -- Novel methods on visualization-oriented data mining -- Visual representations and interaction techniques of data mining results -- Data management and knowledge representation including scalable data representations -- Mathematical foundations and algorithms in data mining to allow interactive visual analysis -- Analytical reasoning including the human analytic, knowledge discovery, perception, and collaborative visual analytics -- Evaluation methods for data mining algorithms and visual analytics systems -- Applications of visual analytics and data mining techniques, including but not limited to applications in science, engineering, public safety, commerce, etc.
FacetGist: Collective Extraction of Document Facets in Large Technical Corpora
Authors: Tarique Siddiqui, Xiang Ren, Aditya G. Parameswaran, Jiawei Han
Venue: CIKM
Year: 2016

Given the large volume of technical documents available, it is crucial to automatically organize and categorize these documents to be able to understand and extract value from them. Towards this end, we introduce a new research problem called Facet Extraction. Given a collection of technical documents, the goal of Facet Extraction is to automatically label each document with a set of concepts for the key facets (<i>e.g.</i>, application, technique, evaluation metrics, and dataset) that people may be interested in. Facet Extraction has numerous applications, including document summarization, literature search, patent search and business intelligence. The major challenge in performing Facet Extraction arises from multiple sources: concept extraction, concept to facet matching, and facet disambiguation. To tackle these challenges, we develop FacetGist, a framework for facet extraction. Facet Extraction involves constructing a graph-based heterogeneous network to capture information available across multiple <i>local</i> sentence-level features, as well as <i>global</i> context features. We then formulate a joint optimization problem, and propose an efficient algorithm for graph-based label propagation to estimate the facet of each concept mention. Experimental results on technical corpora from two domains demonstrate that Facet Extraction can lead to an improvement of over 25% in both precision and recall over competing schemes.
Compression-Based Selective Sampling for Learning to Rank
Authors: Rodrigo M. Silva, Guilherme de Castro Mendes Gomes, Mário S. Alvim, Marcos André Gonçalves
Venue: CIKM
Year: 2016

Learning to rank (L2R) algorithms use a labeled training set to generate a ranking model that can be later used to rank new query results. These training sets are very costly and laborious to produce, requiring human annotators to assess the relevance or order of the documents in relation to a query. Active learning (AL) algorithms are able to reduce the labeling effort by actively sampling an unlabeled set and choosing data instances that maximize the effectiveness of a learning function. But AL methods require constant supervision, as documents have to be labeled at each round of the process. In this paper, we propose that certain characteristics of unlabeled L2R datasets allow for an unsupervised, compression-based selection process to be used to create small and yet highly informative and effective initial sets that can later be labeled and used to bootstrap a L2R system. We implement our ideas through a novel unsupervised selective sampling method, which we call Cover, that has several advantages over AL methods tailored to L2R. First, it does not need an initial labeled seed set and can select documents from scratch. Second, selected documents do not need to be labeled as the iterations of the method progress since it is unsupervised (i.e., no learning model needs to be updated). Thus, an arbitrarily sized training set can be selected without human intervention depending on the available budget. Third, the method is efficient and can be run on unlabeled collections containing millions of query-document instances. We run various experiments with two important L2R benchmarking collections to show that the proposed method allows for the creation of small, yet very effective training sets. It achieves full training-like performance with less than 10% of the original sets selected, outperforming the baselines in both effectiveness and scalability.
Iterative Search using Query Aspects
Authors: Manmeet Singh, W. Bruce Croft
Venue: CIKM
Year: 2016

Pseudo-relevance feedback (PRF) via query expansion has proven to be effective in many information retrieval tasks. In most existing work, the top-ranked documents from an initial search are assumed to be relevant and used for feedback. There are some drawbacks to this approach. One limitation is that there might be other relevant documents which were not retrieved or considered for the the feedback process. Another issue is one or more of the top retrieved documents may be non-relevant, which can introduce noise into the feedback mechanism. Term-level diversification, on the other hand, uses an effective technique for identifying terms associated with query aspects or subtopics. We propose a new iterative feedback method that combines PRF with aspect generation to improve feedback effectiveness. In our experiments, we discovered a new property of convergence of feedback terms that was incorporated into the PRF process. We show that the resulting method significantly outperforms the baseline relevance model.
Semi-supervised Multi-Label Topic Models for Document Classification and Sentence Labeling
Authors: Hossein Soleimani, David J. Miller
Venue: CIKM
Year: 2016

Extracting parts of a text document relevant to a class label is a critical information retrieval task. We propose a semi-supervised multi-label topic model for jointly achieving document and sentence-level class inferences. Under our model, each sentence is associated with only a subset of the document's labels (including possibly none of them), with the label set of the document the union of the labels of all of its sentences. For training, we use both labeled documents, and, typically, a larger set of unlabeled documents. Our model, in a semisupervised fashion, discovers the topics present, learns associations between topics and class labels, predicts labels for new (or unlabeled) documents, and determines label associations for each sentence in every document. For learning, our model does not require any ground-truth labels on sentences. We develop a Hamiltonian Monte Carlo based algorithm for efficiently sampling from the joint label distribution over all sentences, a very high-dimensional discrete space. Our experiments show that our approach outperforms several benchmark methods with respect to both document and sentence-level classification, as well as test set log-likelihood. All code for replicating our experiments is available from https://github.com/hsoleimani/MLTM.
Targeted Influence Maximization in Social Networks
Authors: Chonggang Song, Wynne Hsu, Mong-Li Lee
Venue: CIKM
Year: 2016

Influence maximization (IM) problem asks for a set of k nodes in a given graph G, such that it can reach the largest expected number of remaining nodes in G. Existing methods have either considered that the influence be targeted to meet certain deadline constraint, or be restricted to specific geographical region. However, if an event organizer wants to disseminate some event information on a social platform, s/he would want to select a set of users who can influence the most number of people within the neighborhood of the event location, and this influence should occur before the event takes place. Considering the location and deadline independently may lead to a less than optimal set of users. In this paper, we formalize the problem targeted influence maximization in social networks. We adopt a login model where each user is associated with a login probability and he can be influenced by his neighbors only when he is online. We develop a sampling based algorithm that returns a (1-1/e-ε)-approximate solution, as well as an efficient heuristic algorithm that focuses on nodes close to the target location. Experiments on real-world social network datasets demonstrate the effectiveness and efficiency of our proposed method.