OpenRaiser/Intern-Atlas huggingface dataset:
Minimizing Dependence between Graphs
Authors: Yu Rong, Hong Cheng
Venue: CIKM
Year: 2017

In recent years, modeling the relation between two graphs has received unprecedented attention from researchers due to its wide applications in many areas, such as social analysis and bioinformatics. The nature of relations between two graphs can be divided into two categories: the vertex relation and the link relation. Many studies focus on modeling the vertex relation between graphs and try to find the vertex correspondence between two graphs. However, the link relation between graphs has not been fully studied. Specifically, we model the cross-graph link relation as cross-graph dependence, which reflects the dependence of a vertex in one graph on a vertex in the other graph. A generic problem, called Graph Dependence Minimization (GDM), is defined as: given two graphs with cross-graph dependence, how to select a subset of vertexes from one graph and copy them to the other, so as to minimize the cross-graph dependence. Many real applications can benefit from the solution to GDM. Examples include reducing the cross-language links in online encyclopedias, optimizing the cross-platform communication cost between different cloud services, and so on. This problem is trivial if we can select as many vertexes as we want to copy. But what if we can only choose a limited number of vertexes to copy so as to make the two graphs as independent as possible? We formulate GDM with a budget constraint into a combinatorial optimization problem, which is proven to be NP-hard. We propose two algorithms to solve GDM. Firstly, we prove the submodularity of the objective function of GDM and adopt the size-constrained submodular minimization (SSM) algorithm to solve it. Since the SSM-based algorithm cannot scale to large graphs, we design a heuristic algorithm with a provable approximation guarantee. We prove that the error achieved by the heuristic algorithm is bounded by an additive factor which is proportional to the square of the given budget. Extensive experiments on both synthetic and real-world graphs show that the proposed algorithms consistently outperform the well-studied graph centrality measure based solutions. Furthermore, we conduct a case study on the Wikipedia graphs with millions of vertexes and links to demonstrate the potential of GDM to solve real-world problems.
Combining Local and Global Word Embeddings for Microblog Stemming
Authors: Anurag Roy, Trishnendu Ghorai, Kripabandhu Ghosh, Saptarshi Ghosh
Venue: CIKM
Year: 2017

Stemming is a vital step employed to improve retrieval performance through efficient unification of morphological variants of a word. We propose an unsupervised, context-specific stemming algorithm for microblogs, based on both local and global word embeddings, which is capable of handling the informal, noisy vocabulary of microblogs. Experiments on two standard microblog data collections (TREC 2016 and FIRE 2016) show that, the proposed stemmer enables significantly better retrieval performance than several state-of-the-art stemming algorithms, for the same queries.
CSI: A Hybrid Deep Model for Fake News Detection
Authors: Natali Ruchansky, Sungyong Seo, Yan Liu
Venue: CIKM
Year: 2017

The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opportunity for malicious parties to manipulate the outcomes of public events such as elections. Because such high stakes are at play, automatically detecting fake news is an important, yet challenging problem that is not yet well understood. Nevertheless, there are three generally agreed upon characteristics of fake news: the text of an article, the user response it receives, and the source users promoting it. Existing work has largely focused on tailoring solutions to one particular characteristic which has limited their success and generality. In this work, we propose a model that combines all three characteristics for a more accurate and automated prediction. Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news. Motivated by the three characteristics, we propose a model called CSI which is composed of three modules: Capture, Score, and Integrate. The first module is based on the response and text; it uses a Recurrent Neural Network to capture the temporal pattern of user activity on a given article. The second module learns the source characteristic based on the behavior of users, and the two are integrated with the third module to classify an article as fake or not. Experimental analysis on real-world data demonstrates that CSI achieves higher accuracy than existing models, and extracts meaningful latent representations of both users and articles.
Regularized and Retrofitted models for Learning Sentence Representation with Context
Authors: Tanay Kumar Saha, Shafiq R. Joty, Naeemul Hassan, Mohammad Al Hasan
Venue: CIKM
Year: 2017

Vector representation of sentences is important for many text processing tasks that involve classifying, clustering, or ranking sentences. For solving these tasks, bag-of-word based representation has been used for a long time. In recent years, distributed representation of sentences learned by neural models from unlabeled data has been shown to outperform traditional bag-of-words representations. However, most existing methods belonging to the neural models consider only the content of a sentence, and disregard its relations with other sentences in the context. In this paper, we first characterize two types of contexts depending on their scope and utility. We then propose two approaches to incorporate contextual information into content-based models. We evaluate our sentence representation models in a setup, where context is available to infer sentence vectors. Experimental results demonstrate that our proposed models outshine existing models on three fundamental tasks, such as, classifying, clustering, and ranking sentences.
IMaxer: A Unified System for Evaluating Influence Maximization in Location-based Social Networks
Authors: Muhammad Aamir Saleem, Rohit Kumar, Toon Calders, Xike Xie, Torben Bach Pedersen
Venue: CIKM
Year: 2017

Due to the popularity of social networks with geo-tagged activities, so-called location-based social networks (LBSN), a number of methods have been proposed for influence maximization for applications such as word-of-mouth marketing (WOMM), and out-of-home marketing (OOH). It is thus important to analyze and compare these different approaches. In this demonstration, we present a unified system IMaxer that both provides a complete pipeline of state-of-the-art and novel models and algorithms for influence maximization (IM) as well as allows to evaluate and compare IM techniques for a particular scenario. IMaxer allows to select and transform the required data from raw LBSN datasets. It further provides a unified model that utilizes interactions of nodes in an LBSN, i.e., users and locations, for capturing diverse types of information propagations. On the basis of these interactions, influential nodes can be found and their potential influence can be simulated and visualized using Google Maps and graph visualization APIs. Thus, IMaxer allows users to compare and pick the most suitable IM method in terms of effectiveness and cost.
Revealing the Hidden Links in Content Networks: An Application to Event Discovery
Authors: Antonia Saravanou, Ioannis Katakis, George Valkanas, Vana Kalogeraki, Dimitrios Gunopulos
Venue: CIKM
Year: 2017

Social networks have become the de facto online resource for people to share, comment on and be informed about events pertinent to their interests and livelihood, ranging from road traffic or an illness to concerts and earthquakes, to economics and politics. This has been the driving force behind research endeavors that analyse such data. In this paper, we focus on how Content Networks can help us identify events effectively. Content Networks incorporate both structural and content-related information of a social network in a unified way, at the same time, bringing together two disparate lines of research: graph-based and content-based event discovery in social media. We model interactions of two types of nodes, users and content, and introduce an algorithm that builds heterogeneous, dynamic graphs, in addition to revealing content links in the network's structure. By linking similar content nodes and tracking connected components over time, we can effectively identify different types of events. Our evaluation on social media streaming data suggests that our approach outperforms state-of-the-art techniques, while showcasing the significance of hidden links to the quality of the results.
When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes
Authors: Arun V. Sathanur, Sutanay Choudhury, Cliff A. Joslyn, Sumit Purohit
Venue: CIKM
Year: 2017

Property graphs can be used to represent heterogeneous networks with labeled (attributed) vertices and edges. Given a property graph, simulating another graph with same or greater size with the same statistical properties with respect to the labels and connectivity is critical for privacy preservation and benchmarking purposes. In this work we tackle the problem of capturing the statistical dependence of the edge connectivity on the vertex labels and using the same distribution to regenerate property graphs of the same or expanded size in a scalable manner. However, accurate simulation becomes a challenge when the attributes do not completely explain the network structure. We propose the Property Graph Model (PGM) approach that uses a label augmentation strategy to mitigate the problem and preserve the vertex label and the edge connectivity distributions as well as their correlation, while also replicating the degree distribution. Our proposed algorithm is scalable with a linear complexity in the number of edges in the target graph. We illustrate the efficacy of the PGM approach in regenerating and expanding the datasets by leveraging two distinct illustrations. Our open-source implementation is available on GitHub.
Fast and Accurate Time Series Classification with WEASEL
Authors: Patrick Schäfer, Ulf Leser
Venue: CIKM
Year: 2017

Time series (TS) occur in many scientific and commercial applications, ranging from earth surveillance to industry automation to the smart grids. An important type of TS analysis is classification, which can, for instance, improve energy load forecasting in smart grids by detecting the types of electronic devices based on their energy consumption profiles recorded by automatic sensors. Such sensor-driven applications are very often characterized by (a) very long TS and (b) very large TS datasets needing classification. However, current methods to time series classification (TSC) cannot cope with such data volumes at acceptable accuracy; they are either scalable but offer only inferior classification quality, or they achieve state-of-the-art classification quality but cannot scale to large data volumes. In this paper, we present WEASEL (Word ExtrAction for time SEries cLassification), a novel TSC method which is both fast and accurate. Like other state-of-the-art TSC methods, WEASEL transforms time series into feature vectors, using a sliding-window approach, which are then analyzed through a machine learning classifier. The novelty of WEASEL lies in its specific method for deriving features, resulting in a much smaller yet much more discriminative feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more accurate than the best current non-ensemble algorithms at orders-of-magnitude lower classification and training times, and it is almost as accurate as ensemble classifiers, whose computational complexity makes them inapplicable even for mid-size datasets. The outstanding robustness of WEASEL is also confirmed by experiments on two real smart grid datasets, where it out-of-the-box achieves almost the same accuracy as highly tuned, domain-specific methods.
pm-SCAN: an I/O Efficient Structural Clustering Algorithm for Large-scale Graphs
Authors: Jung Hyuk Seo, Myoung Ho Kim
Venue: CIKM
Year: 2017

Most existing algorithms for graph clustering, including SCAN, are not designed to cope with large volumes of data that cannot fit in main memory. When there is not enough memory, those algorithms will incur thrashing, i.e. result in huge I/O costs. We propose an I/O-efficient algorithm for structural clustering, pm-SCAN. The main idea of our scheme is to partition a large graph into several subgraphs that can fit into main memory. We first find clusters in each subgraph, and then merge them to produce final clustering of the input graph. Experimental results show that while other existing algorithms are not scalable to the graph size, our proposed method produces scalable performance for limited memory space.
StreamingCube: A Unified Framework for Stream Processing and OLAP Analysis
Authors: Salman Ahmed Shaikh, Hiroyuki Kitagawa
Venue: CIKM
Year: 2017

In most streaming applications, the data streams need to be analyzed continuously to make instant decisions exploiting latest information. Often data streams are multidimensional and are at the low-level of abstraction, whereas analysts are interested in multi-level interactive analysis of data streams across several dimensions. On-line analytical processing (OLAP) is a proven technique for such analysis of static data and has also been studied by some researchers for data streams. Traditionally this is achieved by coupling a stream processing engine with an OLAP engine. We believe that coupling multiple systems is not an efficient solutions as it results in lower performance (due to the transfer of data between multiple systems), resource wastage (due to replication of data for each coupled system) and increased complexity and maintenance cost. To this end, we present StreamingCube, a unified framework for data stream processing and its interactive OLAP analysis. The proposed framework possesses all the essential operators to process data streams and introduces a new operator, cubify, to maintain OLAP lattice nodes (materialized views) incrementally. The novelty of the introduced cubify operator lies in the incremental maintenance of the materialized views. To demonstrate StreamingCube, a web-based GUI has been developed which enables users to register continuous queries (CQs). Once a CQ has been registered, users can perform different OLAP operations through the GUI for the interactive analysis. The results of the OLAP queries/operations are displayed in the form of tables and graphs.
Knowledge Graph Embedding with Triple Context
Authors: Jun Shi, Huan Gao, Guilin Qi, Zhangquan Zhou
Venue: CIKM
Year: 2017

Knowledge graph embedding, which aims to represent entities and relations in vector spaces, has shown outstanding performance on a few knowledge graph completion tasks. Most existing methods are based on the assumption that a knowledge graph is a set of separate triples, ignoring rich graph features, i.e., structural information in the graph. In this paper, we take advantages of structures in knowledge graphs, especially local structures around a triple, which we refer to as triple context. We then propose a Triple-Context-based knowledge Embedding model (TCE). For each triple, two kinds of structure information are considered as its context in the graph; one is the outgoing relations and neighboring entities of an entity and the other is relation paths between a pair of entities, both of which reflect various aspects of the triple. Triples along with their contexts are represented in a unified framework, in which way structural information in triple contexts can be embodied. The experimental results show that our model outperforms the state-of-the-art methods for link prediction.
Modeling Student Learning Styles in MOOCs
Authors: Yuling Shi, Zhiyong Peng, Hongning Wang
Venue: CIKM
Year: 2017

The recorded student activities in Massive Open Online Course (MOOC) provide us a unique opportunity to model their learning behaviors, identify their particular learning intents, and enable personalized assistance and guidance in online education. In this work, based on a thorough qualitative study of students' behaviors recorded in two MOOC courses with large student enrollments, we develop a non-parametric Bayesian model to capture students' sequential learning activities in a generative manner. Homogeneity of students' learning behaviors is captured by clustering them into latent student groups, where shared model structure characterizes the transitional patterns, intensity and temporal distribution of their learning activities. In the meanwhile, heterogeneity is captured by clustering students into different groups. Both qualitative and quantitative studies on those two MOOC courses confirmed the effectiveness of the proposed model in identifying students' learning behavior patterns and clustering them into related groups for predictive analysis. The identified student groups accurately predict student retention, course satisfaction and demographics.
A Neural Candidate-Selector Architecture for Automatic Structured Clinical Text Annotation
Authors: Gaurav Singh, Iain James Marshall, James Thomas, John Shawe-Taylor, Byron C. Wallace
Venue: CIKM
Year: 2017

We consider the task of automatically annotating free texts describing clinical trials with concepts from a controlled, structured medical vocabulary. Specifically we aim to build a model to infer distinct sets of (ontological) concepts describing complementary clinically salient aspects of the underlying trials: the populations enrolled, the interventions administered and the outcomes measured, i.e., the <i>PICO</i> elements. This important practical problem poses a few key challenges. One issue is that the output space is vast, because the vocabulary comprises many unique concepts. Compounding this problem, annotated data in this domain is expensive to collect and hence sparse. Furthermore, the outputs (sets of concepts for each PICO element) are correlated: specific populations (e.g., diabetics) will render certain intervention concepts likely (insulin therapy) while effectively precluding others (radiation therapy). Such correlations should be exploited. We propose a novel neural model that addresses these challenges. We introduce a Candidate-Selector architecture in which the model considers setes of <i>candidate concepts</i> for PICO elements, and assesses their plausibility conditioned on the input text to be annotated. This relies on a 'candidate set' generator, which may be learned or relies on heuristics. A conditional discriminative neural model then jointly selects candidate concepts, given the input text. We compare the predictive performance of our approach to strong baselines, and show that it outperforms them. Finally, we perform a qualitative evaluation of the generated annotations by asking domain experts to assess their quality.
SMASC 2017: First International Workshop on Social Media Analytics for Smart Cities
Authors: Manjira Sinha, Xiangnan He, Alessandro Bozzon, Sandya Mannarswamy, Pradeep K. Murukannaiah, Tridib Mukherjee
Venue: CIKM
Year: 2017

In an increasingly digital urban setting, connected & concerned Citizens typically voice their opinions on various civic topics via social media. Efficient and scalable analysis of these citizen voices on social media to derive actionable insights is essential to the development of smart cities. The very nature of the data: heterogeneity and dynamism, the scarcity of gold standard annotated corpora, and the need for multi-dimensional analysis across space, time and semantics, makes urban social media analytics challenging. This workshop is dedicated to the theme of social media analytics for smart cities, with the aim of focusing the interest of CIKM research community on the challenges in mining social media data for urban informatics. The workshop hopes to foster collaboration between researchers working in information retrieval, social media analytics, linguistics; social scientists, and civic authorities, to develop scalable and practical systems for capturing and acting upon real world issues of cities as voiced by their citizens in social media. The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications for smart cities.
Product Exploration based on Latent Visual Attributes
Authors: Tomás Skopal, Ladislav Peska, Gregor Kovalcík, Tomás Grosup, Jakub Lokoc
Venue: CIKM
Year: 2017

In this demo paper, we present a prototype web application of a product search engine of a fashion e-shop. Although e-shop products consist of full-text description, relational attributes (e.g., price, type, size, color, etc.) as well as visual information (product photo), traditional search engines in e-shops only provide full-text and relational attributes for product filtering. In our retrieval model, we incorporate also the visual information into the search by extracting visual-semantic features using deep convolutional neural networks. Furthermore, visual exploration of the product space using the visual-semantic features (multi-example queries) is used to dynamically discover latent visual attributes that could enhance the original relational schema by fuzzy attributes (e.g., a floral pattern in product). In the demo, we show how these latent attributes could be used to recommend the user preferred products and even outfits (e.g., shoes, bag, jacket) that fit a certain visual style.
Denoising Clinical Notes for Medical Literature Retrieval with Convolutional Neural Model
Authors: Luca Soldaini, Andrew Yates, Nazli Goharian
Venue: CIKM
Year: 2017

The rapid increase of medical literature poses a significant challenge for physicians, who have repeatedly reported to struggle to keep up to date with developments in research. This gap is one of the main challenges in integrating recent advances in clinical research with day-to-day practice. Thus, the need for clinical decision support (CDS) search systems that can retrieve highly relevant medical literature given a clinical note describing a patient has emerged. However, clinical notes are inherently noisy, thus not being fit to be used as queries as-is. In this work, we present a convolutional neural model aimed at improving clinical notes representation, making them suitable for document retrieval. The system is designed to predict, for each clinical note term, its importance in relevant documents. The approach was evaluated on the 2016 TREC CDS dataset, where it achieved a 37% improvement in infNDCG over state-of-the-art query reduction methods and a 27% improvement over the best known method for the task.
Volume Ranking and Sequential Selection in Programmatic Display Advertising
Authors: Yuxuan Song, Kan Ren, Han Cai, Weinan Zhang, Yong Yu
Venue: CIKM
Year: 2017

Programmatic display advertising, which enables advertisers to make real-time decisions on individual ad display opportunities so as to achieve a precise audience marketing, has become a key technique for online advertising. However, the constrained budget setting still restricts unlimited ad impressions. As a result, a smart strategy for ad impression selection is necessary for the advertisers to maximize positive user responses such as clicks or conversions, under the constraints of both ad volume and campaign budget. In this paper, we borrow in the idea of top-N ranking and filtering techniques from information retrieval and propose an effective ad impression volume ranking method for each ad campaign, followed by a sequential selection strategy considering the remaining ad volume and budget, to smoothly deliver the volume filtering while maximizing campaign efficiency. The extensive experiments on two benchmarking datasets and a commercial ad platform demonstrate large performance superiority of our proposed solution over traditional methods, especially under tight budgets.