OpenRaiser/Intern-Atlas huggingface dataset:
Modeling Individual-Level Infection Dynamics Using Social Network Information
Authors: Suppawong Tuarob, Conrad S. Tucker, Marcel Salathé, Nilam Ram
Venue: CIKM
Year: 2015

Epidemic monitoring systems engaged in accurate discovery of infected individuals enable better understanding of the dynamics of epidemics and thus may promote effective disease mitigation or prevention. Currently, infection discovery systems require either physical participation of potential patients or provision of information from hospitals and health-care services. While social media has emerged as an increasingly important knowledge source that reflects multiple real world events, there is only a small literature examining how social media information can be incorporated into computational epidemic models. In this paper, we demonstrate how social media information can be incorporated into and improve upon traditional techniques used to model the dynamics of infectious diseases. Using flu infection histories and social network data collected from 264 students in a college community, we identify social network signals that can aid identification of infected individuals. Extending the traditional SIRS model, we introduce and illustrate the efficacy of an Online-Interaction-Aware Susceptible-Infected-Recovered-Susceptible (OIA-SIRS) model based on four social network signals for modeling infection dynamics. Empirical evaluations of our case study, flu infection within a college community, reveal that the OIA-SIRS model is more accurate than the traditional model, and also closely tracks the real-world infection rates as reported by CDC ILINet and Google Flu Trend.
DifRec: A Social-Diffusion-Aware Recommender System
Authors: Hossein Vahabi, Iordanis Koutsopoulos, Francesco Gullo, Maria Halkidi
Venue: CIKM
Year: 2015

Recommender systems used in current online social platforms make recommendations by only considering how relevant an item is to a specific user but they ignore the fact that, thanks to mechanisms like sharing or re-posting across the underlying social network, an item recommended to a user i propagates through the network and can reach another user j without needing to be explicitly recommended to j too. Overlooking this fact may lead to an inefficient use of the limited recommendation slots. These slots can instead be exploited more profitably by avoiding unnecessary duplicates and recommending other equally relevant items.
Characterizing and Predicting Viral-and-Popular Video Content
Authors: David Vallet, Shlomo Berkovsky, Sebastien Ardon, Anirban Mahanti, Mohamed Ali Kâafar
Venue: CIKM
Year: 2015

The proliferation of online video content has triggered numerous works on its evolution and popularity, as well as on the effect of social sharing on content propagation. In this paper, we focus on the observable dependencies between the virality of video content on a micro-blogging social network (in this case, Twitter) and the popularity of such content on a video distribution service (YouTube). To this end, we collected and analysed a corpus of Twitter posts containing links to YouTube clips and the corresponding video meta-data from YouTube. Our analysis highlights the unique properties of content that is both popular and viral, which allows such content to attract high number of views on YouTube and achieve fast propagation on Twitter. With this in mind, we proceed to the predictions of popular-and-viral clips and propose a framework that can, with high degree of accuracy and low amount of training data, predict videos that are likely to be popular, viral, and both. The key contribution of our work is the focus on cross-system dynamics between YouTube and Twitter. We conjecture and validate that cross-system prediction of both popularity and virality of videos is feasible, and can be performed with a reasonably high degree of accuracy. One of our key findings is that YouTube features capturing user engagement, have strong virality prediction capabilities. This findings allows to solely rely on data extracted from a video sharing service to predict popularity and virality aspects of videos.
Parallel Lazy Semi-Naive Bayes Strategies for Effective and Efficient Document Classification
Authors: Felipe Viegas, Marcos André Gonçalves, Wellington Martins, Leonardo Rocha
Venue: CIKM
Year: 2015

Automatic Document Classification (ADC) is the basis of many important applications such as spam filtering and content organization. Naive Bayes (NB) approaches are a widely used classification paradigm, due to their simplicity, efficiency, absence of parameters and effectiveness. However, they do not present competitive effectiveness when compared to other modern statistical learning methods, such as SVMs. This is related to some characteristics of real document collections, such as class imbalance, feature sparseness and strong relationships among attributes. In this paper, we investigate whether the relaxation of the NB feature independence assumption (aka, Semi-NB approaches) can improve its effectiveness in large text collections. We propose four new Lazy Semi-NB strategies that exploit different ideas for alleviating the NB independence assumption. By being lazy, our solutions focus only on the most important features to classify a given test document, overcoming some Semi-NB issues when applied to ADC such as bias towards larger classes and overfitting and/or lack of generalization of the models. We demonstrate that our Lazy Semi-NB proposals can produce superior effectiveness when compared to state-of-the-art ADC classifiers such as SVM and KNN. Moreover, to overcome some efficiency issues of combining Semi-NB and lazy strategies, we take advantage of current manycore GPU architectures and present a massively parallelized version of the Semi-NB approaches. Our experimental results show that speedups of up to 63.36 times can be obtained when compared to serial solutions, making our proposals very practical in real-situations.
Classification with Active Learning and Meta-Paths in Heterogeneous Information Networks
Authors: Chang Wan, Xiang Li, Ben Kao, Xiao Yu, Quanquan Gu, David Wai-Lok Cheung, Jiawei Han
Venue: CIKM
Year: 2015

A heterogeneous information network (HIN) is used to model objects of different types and their relationships. Meta-paths are sequences of object types. They are used to represent complex relationships between objects beyond what links in a homogeneous network capture. We study the problem of classifying objects in an HIN. We propose class-level meta-paths and study how they can be used to (1) build more accurate classifiers and (2) improve active learning in identifying objects for which training labels should be obtained. We show that class-level meta-paths and object classification exhibit interesting synergy. Our experimental results show that the use of class-level meta-paths results in very effective active learning and good classification performance in HINs.
Concept-Based Relevance Models for Medical and Semantic Information Retrieval
Authors: Chunye Wang, Ramakrishna Akella
Venue: CIKM
Year: 2015

Relevance models provide an important approach for estimating probabilities of words in the relevant class. However, the associated bag-of-words assumption breaks dependencies between words, especially between those within a phrase. If such dependencies could be preserved, it would permit matching the query terms with document terms having the same dependencies. Additionally, during the estimation of relevance, relevance models are unable to distinguish relevant and non-relevant information in a feedback document, and hence take the entire document into account, which potentially hurts the accuracy of estimation. In this paper, we define the notion of "concept", and design a concept-based information retrieval framework. Using this framework, we transform documents and queries from term space into concept space, and propose a concept-based relevance model for improved estimation of relevance. Our approach has three advantages. First, this approach only assumes independence between concepts, so is able to keep the strong dependencies between the words of a concept. Second, it unifies synonyms or different surface forms of a concept, leading to reduced dimensionality of the space, increased sample size of a concept, and consequently more accurate and reliable estimates of the relevance. Third, when knowledge bases are available, our approach enables the semantic analysis of query concepts, and thus identifies concepts related to the query, from which a more accurate distribution of relevance can be estimated. This work is aligned with semantic search methods.
Towards Scalable and Complete Query Explanation with OWL 2 EL Ontologies
Authors: Zhe Wang, Mahsa Chitsaz, Kewen Wang, Jianfeng Du
Venue: CIKM
Year: 2015

Ontology-mediated data access and management systems are rapidly emerging. Besides standard query answering, there is also a need for such systems to be coupled with explanation facilities, in particular to explain missing query answers (i.e. desired answers of a query which are not derivable from the given ontology and data). This support is highly demanded for debugging and maintenance of big data, and both theoretical results and algorithms proposed. However, existing query explanation algorithms either cannot scale over relative large data sets or are not guaranteed to compute all desired explanations. To the best of our knowledge, no existing algorithm can efficiently and completely explain conjunctive queries (CQs) w.r.t. ELH1 ontologies. In this paper, we present a hybrid approach to achieve this. An implementation of the proposed query explanation algorithm has been developed using an off-the-shelf Prolog engine and a datalog engine. Finally, the system is evaluated over practical ontologies. Experimental results show that our system scales over large data sets.
ASEM: Mining Aspects and Sentiment of Events from Microblog
Authors: Ruhui Wang, Weijing Huang, Wei Chen, Tengjiao Wang, Kai Lei
Venue: CIKM
Year: 2015

Microblogs contain the most up-to-date and abundant opinion information on current events. Aspect-based opinion mining is a good way to get a comprehensive summarization of events. The most popular aspect based opinion mining models are used in the field of product and service. However, existing models are not suitable for event mining. In this paper we propose a novel probabilistic generative model (ASEM) to simultaneously discover aspects and the specified opinions. ASEM incorporate a sequence labeling model(CRF) into a generative topic model. Additionally, we adopt a set of features for separating aspects and sentiments. Moreover, we novelly present a continuously learning model. It can utilize the knowledge of one event to learn another, and get a better performance. We use five real world events to do experiment. The experimental results show that ASEM extracts aspects and sentiments well, and ASEM outperforms other state-of-art models and the intuitive two-step method.
Viewability Prediction for Online Display Ads
Authors: Chong Wang, Achir Kalra, Cristian Borcea, Yi Chen
Venue: CIKM
Year: 2015

As a massive industry, display advertising delivers advertisers' marketing messages to attract customers through graphic banners on webpages. Advertisers are charged by ad serving, where their ads are shown in web pages. However, recent studies show that about half of the ads were actually never seen by users because they do not scroll deep enough to bring the ads in-view. Thus, the ad pricing standards are shifting to a new model: ads are paid if they are in view, not just being served. To the best of our knowledge, this paper is the first to address the important problem of ad viewability prediction which can improve the performance of guaranteed ad delivery, real-time bidding, as well as recommender systems. We analyze a real-life dataset from a large publisher, identify a number of features that impact the scroll depth for a given user and a page, and propose a probabilistic latent class model that predicts the viewability of any given scroll depth for a user-page pair. The experiments demonstrate that our model outperforms comparison systems based on singular value decomposition and logistic regression, in terms of prediction quality and training time.
Approximate Truth Discovery via Problem Scale Reduction
Authors: Xianzhi Wang, Quan Z. Sheng, Xiu Susie Fang, Xue Li, Xiaofei Xu, Lina Yao
Venue: CIKM
Year: 2015

Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for each data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a user-specified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.
Update Summarization using Semi-Supervised Learning Based on Hellinger Distance
Authors: Dingding Wang, Sahar Sohangir, Tao Li
Venue: CIKM
Year: 2015

Update summarization aims to generate brief summaries of recent documents to capture new information different from earlier documents. In this paper, we propose a new method to generate the sentence similarity graph using a novel similarity measure based on Helliger distance and apply semi-supervised learning on the sentence graph to select the sentences with maximum consistency and minimum redundancy to form the summaries. We use TAC 2011 data to evaluate our proposed method and compare it with existing baselines. The experimental results show the effectiveness of our proposed method.
Enhanced Word Embeddings from a Hierarchical Neural Language Model
Authors: Xun Wang, Katsuhito Sudoh, Masaaki Nagata
Venue: CIKM
Year: 2015

This paper proposes a neural language model to capture the interaction of text units of different levels, i.e.., documents, paragraphs, sentences, words in an hierarchical structure. At each paralleled level, the model incorporates Markov property while each higher-level unit hierarchically influences its containing units. Such an architecture enables the learned word embeddings to encode both global and local information. We evaluate the learned word embeddings and experiments demonstrate the effectiveness of our model.
Multi-view Clustering via Structured Low-rank Representation
Authors: Dong Wang, Qiyue Yin, Ran He, Liang Wang, Tieniu Tan
Venue: CIKM
Year: 2015

In this paper, we present a novel solution to multi-view clustering through a structured low-rank representation. When assuming similar samples can be linearly reconstructed by each other, the resulting representational matrix reflects the cluster structure and should ideally be block diagonal. We first impose low-rank constraint on the representational matrix to encourage better grouping effect. Then representational matrices under different views are allowed to communicate with each other and share their mutual cluster structure information. We develop an effective algorithm inspired by iterative re-weighted least squares for solving our formulation. During the optimization process, the intermediate representational matrix from one view serves as a cluster structure constraint for that from another view. Such mutual structural constraint fine-tunes the cluster structures from both views and makes them more and more agreeable. Extensive empirical study manifests the superiority and efficacy of the proposed method.
Large-Scale Question Answering with Joint Embedding and Proof Tree Decoding
Authors: Zhenghao Wang, Shengquan Yan, Huaming Wang, Xuedong Huang
Venue: CIKM
Year: 2015

Question answering (QA) over a large-scale knowledge base (KB) such as Freebase is an important natural language processing application. There are linguistically oriented semantic parsing techniques and machine learning motivated statistical methods. Both of these approaches face a key challenge on how to handle diverse ways natural questions can be expressed about predicates and entities in the KB. This paper is to investigate how to combine these two approaches. We frame the problem from a proof-theoretic perspective, and formulate it as a proof tree search problem that seamlessly unifies semantic parsing, logic reasoning, and answer ranking. We combine our word entity joint embedding learned from web-scale data with other surface-form features to further boost accuracy improvements. Our real-time system on the Freebase QA task achieved a very high F1 score (47.2) on the standard Stanford WebQuestions benchmark test data.
Defragging Subgraph Features for Graph Classification
Authors: Haishuai Wang, Peng Zhang, Ivor W. Tsang, Ling Chen, Chengqi Zhang
Venue: CIKM
Year: 2015

Graph classification is an important tool for analysing structured and semi-structured data, where subgraphs are commonly used as the feature representation. However, the number and size of subgraph features crucially depend on the threshold parameters of frequent subgraph mining algorithms. Any improper setting of the parameters will generate many trivial short-pattern subgraph fragments which dominate the feature space, distort graph classifiers and bury interesting long-pattern subgraphs. In this paper, we propose a new Subgraph Join Feature Selection (SJFS) algorithm. The SJFS algorithm, by forcing graph classifiers to join short-pattern subgraph fragments, can defrag trivial subgraph features and deliver long-pattern interesting subgraphs. Experimental results on both synthetic and real-world social network graph data demonstrate the performance of the proposed method.
Modeling Infinite Topics on Social Behavior Data with Spatio-temporal Dependence
Authors: Peng Wang, Peng Zhang, Chuan Zhou, Zhao Li, Guo Li
Venue: CIKM
Year: 2015

The problem of modeling topics on user behavior data in social networks has been widely studied in social marketing and social emotion analysis, where latent topic models are commonly used as the solutions. The user behavior data are highly related in time and space, which demands new latent topic models that consider both temporal and spatial distances. However, existing topic models either fail to model these two factors simultaneously, or cannot handle the high order dependence among user behaviors. In this paper we present a new nonparametric Bayesian model Time and Space Dependent Chinese Restaurant Processes (TSD-CRP for short). TSD-CRP can auto-select the number of topics and model high-order temporal and spatial dependence behind user behavior data. Empirical results on real-world data sets demonstrate the effectiveness of the proposed method.
Large-scale Knowledge Base Completion: Inferring via Grounding Network Sampling over Selected Instances
Authors: Zhuoyu Wei, Jun Zhao, Kang Liu, Zhenyu Qi, Zhengya Sun, Guanhua Tian
Venue: CIKM
Year: 2015

Constructing large-scale knowledge bases has attracted much attention in recent years, for which Knowledge Base Completion (KBC) is a key technique. In general, inferring new facts in a large-scale knowledge base is not a trivial task. The large number of inferred candidate facts has resulted in the failure of the majority of previous approaches. Inference approaches can achieve high precision for formulas that are accurate, but they are required to infer candidate instances one by one, and extremely large candidate sets bog them down in expensive calculations. In contrast, the existing embedding-based methods can easily calculate similarity-based scores for each candidate instance as opposed to using inference, so they can handle large-scale data. However, this type of method does not consider explicit logical semantics and usually has unsatisfactory precision. To resolve the limitations of the above two types of methods, we propose an approach through Inferring via Grounding Network Sampling over Selected Instances. We first employ an embedding-based model to make the instance selection and generate much smaller candidate sets for subsequent fact inference, which not only narrows the candidate sets but also filters out part of the noise instances. Then, we only make inferences within these candidate sets by running a data-driven inference algorithm on the Markov Logic Network (MLN), which is called Inferring via Grounding Network Sampling (INS). In this process, we especially incorporate the similarity priori generated by embedding-based models into INS to promote the inference precision. The experimental results show that our approach improved [email protected] from 32.911% to 71.692% on the FB15K dataset and achieved much better [email protected] evaluations than state-of-the-art methods.
Social Spammer and Spam Message Co-Detection in Microblogging with Social Context Regularization
Authors: Fangzhao Wu, Jinyun Shu, Yongfeng Huang, Zhigang Yuan
Venue: CIKM
Year: 2015

The popularity of microblogging platforms, such as Twitter, makes them important for information dissemination and sharing. However, they are also recognized as ideal places by spammers to conduct social spamming. Massive social spammers and spam messages heavily hurt the user experience and hinder the healthy development of microblogging systems. Thus, effectively detecting the social spammers and spam messages in microblogging is of great value. Existing studies mainly regard social spammer detection and spam message detection as two separate tasks. However, social spammers and spam messages have strong connections, since social spammers tend to post more spam messages and spam messages have high probabilities to be posted by social spammers. Combining social spammer detection with spam message detection has the potential to boost the performance of each task. In this paper, we propose a unified framework for social spammer and spam message co-detection in microblogging. Our framework utilizes the posting relations between users and messages to combine social spammer detection with spam message detection. In addition, we extract the social relations between users as well as the connections between messages, and incorporate them into our framework as regularization terms over the prediction results. Besides, we introduce an efficient optimization method to solve our framework. Extensive experiments on a real-world microblog dataset demonstrate that our framework can significantly and consistently improve the performance of both social spammer detection and spam message detection.