OpenRaiser/Intern-Atlas huggingface dataset:
PubMed search and exploration with real-time semantic network construction
Authors: Jinoh Oh, Taehoon Kim, Sun Park, Hwanjo Yu
Venue: KDD
Year: 2012

Exploring PubMed to find relevant information is challenging and time-consuming because PubMed typically returns a long list of articles as a result of query. Semantic network helps users to explore a large document collection and to capture key concepts and relationships among the concepts. The semantic network also serves to broaden the user's knowledge and extend query keyword by detecting and visualizing new related concepts or relations hidden in the retrieved documents. The problem of existing semantic network techniques is that they typically produce many redundant relationships, which prevents users from quickly capturing the underlying relationships among concepts. This paper develops an online PubMed search system, which displays semantic networks having no redundant relationships in real-time as a result of query. To do so, we propose an efficient semantic network construction algorithm, which prevents producing redundant relationships during the network construction. Our extensive experiments on actual PubMed data show that the proposed method is significantly faster than the method removing redundant relationships afterward. Our method is implemented and integrated into a relevance feedback PubMed search engine, called RefMed, "http://dm.postech.ac.kr/refmed", and will be demonstrated through the website.
DAGger: clustering correlated uncertain data (to predict asset failure in energy networks)
Authors: Dan Olteanu, Sebastiaan J. van Schaik
Venue: KDD
Year: 2012

DAGger is a clustering algorithm for uncertain data. In contrast to prior work, DAGger can work on arbitrarily correlated data and can compute both exact and approximate clusterings with error guarantees.
NASA: achieving lower regrets and faster rates via adaptive stepsizes
Authors: Hua Ouyang, Alexander G. Gray
Venue: KDD
Year: 2012

The classic Stochastic Approximation (SA) method achieves optimal rates under the black-box model. This optimality does not rule out better algorithms when more information about functions and data is available.
Unexpected challenges in large scale machine learning
Authors: Charles Parker
Venue: KDD
Year: 2012

In machine learning, scale adds complexity. The most obvious consequence of scale is that data takes longer to process. At certain points, however, scale makes trivial operations costly, thus forcing us to re-evaluate algorithms in light of the complexity of those operations. Here, we will discuss one important way a general large scale machine learning setting may differ from the standard supervised classification setting and show some the results of some preliminary experiments highlighting this difference. The results suggest that there is potential for significant improvement beyond obvious solutions.
The missing models: a data-driven approach for learning how networks grow
Authors: Rob Patro, Geet Duggal, Emre Sefer, Hao Wang, Darya Filippova, Carl Kingsford
Venue: KDD
Year: 2012

Probabilistic models of network growth have been extensively studied as idealized representations of network evolution. Models, such as the Kronecker model, duplication-based models, and preferential attachment models, have been used for tasks such as representing null models, detecting anomalies, algorithm testing, and developing an understanding of various mechanistic growth processes. However, developing a new growth model to fit observed properties of a network is a difficult task, and as new networks are studied, new models must constantly be developed. Here, we present a framework, called GrowCode, for the automatic discovery of novel growth models that match user-specified topological features in undirected graphs. GrowCode introduces a set of basic commands that are general enough to encode several previously developed models. Coupling this formal representation with an optimization approach, we show that GrowCode is able to discover models for protein interaction networks, autonomous systems networks, and scientific collaboration networks that closely match properties such as the degree distribution, the clustering coefficient, and assortativity that are observed in real networks of these classes. Additional tests on simulated networks show that the models learned by GrowCode generate distributions of graphs with similar variance as existing models for these classes.
A near-linear time approximation algorithm for angle-based outlier detection in high-dimensional data
Authors: Ninh Pham, Rasmus Pagh
Venue: KDD
Year: 2012

Outlier mining in d-dimensional point sets is a fundamental and well studied data mining task due to its variety of applications. Most such applications arise in high-dimensional domains. A bottleneck of existing approaches is that implicit or explicit assessments on concepts of distance or nearest neighbor are deteriorated in high-dimensional data. Following up on the work of Kriegel et al. (KDD '08), we investigate the use of angle-based outlier factor in mining high-dimensional outliers. While their algorithm runs in cubic time (with a quadratic time heuristic), we propose a novel random projection-based technique that is able to estimate the angle-based outlier factor for all data points in time near-linear in the size of the data. Also, our approach is suitable to be performed in parallel environment to achieve a parallel speedup. We introduce a theoretical analysis of the quality of approximation to guarantee the reliability of our estimation algorithm. The empirical experiments on synthetic and real world data sets demonstrate that our approach is efficient and scalable to very large high-dimensional data sets.
Key lessons learned building recommender systems for large-scale social networks
Authors: Christian Posse
Venue: KDD
Year: 2012

By helping members to connect, discover and share relevant content or find a new career opportunity, recommender systems have become a critical component of user growth and engagement for social networks. The multidimensional nature of engagement and diversity of members on large-scale social networks have generated new infrastructure and modeling challenges and opportunities in the development, deployment and operation of recommender systems. This presentation will address some of these issues, focusing on the modeling side for which new research is much needed while describing a recommendation platform that enables real-time recommendation updates at scale as well as batch computations, and cross-leverage between different product recommendations. Topics covered on the modeling side will include optimizing for multiple competing objectives, solving contradicting business goals, modeling user intent and interest to maximize placement and timeliness of the recommendations, utility metrics beyond CTR that leverage both real-time tracking of explicit and implicit user feedback, gathering training data for new product recommendations, virality preserving online testing and virtual profiling.
Efficient frequent item counting in multi-core hardware
Authors: Pratanu Roy, Jens Teubner, Gustavo Alonso
Venue: KDD
Year: 2012

The increasing number of cores and the rich instruction sets of modern hardware are opening up new opportunities for optimizing many traditional data mining tasks. In this paper we demonstrate how to speed up the performance of the computation of frequent items by almost one order of magnitude over the best published results by matching the algorithm to the underlying hardware architecture.
Finding trendsetters in information networks
Authors: Diego Sáez-Trumper, Giovanni Comarela, Virgílio A. F. Almeida, Ricardo Baeza-Yates, Fabrício Benevenuto
Venue: KDD
Year: 2012

Influential people have an important role in the process of information diffusion. However, there are several ways to be influential, for example, to be the most popular or the first that adopts a new idea. In this paper we present a methodology to find trendsetters in information networks according to a specific topic of interest. Trendsetters are people that adopt and spread new ideas influencing other people before these ideas become popular. At the same time, not all early adopters are trendsetters because only few of them have the ability of propagating their ideas by their social contacts through word-of-mouth. Differently from other influence measures, a trendsetter is not necessarily popular or famous, but the one whose ideas spread over the graph successfully. Other metrics such as node in-degree or even standard Pagerank focus only in the static topology of the network. We propose a ranking strategy that focuses on the ability of some users to push new ideas that will be successful in the future. To that end, we combine temporal attributes of nodes and edges of the network with a Pagerank based algorithm to find the trendsetters for a given topic. To test our algorithm we conduct innovative experiments over a large Twitter dataset. We show that nodes with high in-degree tend to arrive late for new trends, while users in the top of our ranking tend to be early adopters that also influence their social contacts to adopt the new trend.
Practical collapsed variational bayes inference for hierarchical dirichlet process
Authors: Issei Sato, Kenichi Kurihara, Hiroshi Nakagawa
Venue: KDD
Year: 2012

We propose a novel collapsed variational Bayes (CVB) inference for the hierarchical Dirichlet process (HDP). While the existing CVB inference for the HDP variant of latent Dirichlet allocation (LDA) is more complicated and harder to implement than that for LDA, the proposed algorithm is simple to implement, does not require variance counts to be maintained, does not need to set hyper-parameters, and has good predictive performance.
D-INDEX: a web environment for analyzing dependences among scientific collaborators
Authors: Claudio Schifanella, Luigi Di Caro, Mario Cataldi, Marie-Aude Aufaure
Venue: KDD
Year: 2012

In this work, we demonstrate a web application, available at http://d-index.di.unito.it, that permits to analyze the scientific profiles of all the researchers indexed by DBLP by focusing on the collaborations that contributed to define their curricula. The presented application allows the user to analyze the profile of a researcher, her dependence degrees on all the co-authors (along her entire scientific publication history) and to make comparisons among them in terms of dependence patterns. In particular, it is possible to estimate and visualize how much a researcher has benefited from collaboration with another researcher as well as the communities in which she has been involved. Moreover, the application permits to compare, in a single chart, each researcher with all the scientists indexed in DBLP by focusing on their dependences with respect to many other parameters like the total number of papers, the number of collaborations and the length of the scientific careers.
A structural cluster kernel for learning on graphs
Authors: Madeleine Seeland, Andreas Karwath, Stefan Kramer
Venue: KDD
Year: 2012

In recent years, graph kernels have received considerable interest within the machine learning and data mining community. Here, we introduce a novel approach enabling kernel methods to utilize additional information hidden in the structural neighborhood of the graphs under consideration. Our novel structural cluster kernel (SCK) incorporates similarities induced by a structural clustering algorithm to improve state-of-the-art graph kernels. The approach taken is based on the idea that graph similarity can not only be described by the similarity between the graphs themselves, but also by the similarity they possess with respect to their structural neighborhood. We applied our novel kernel in a supervised and a semi-supervised setting to regression and classification problems on a number of real-world datasets of molecular graphs.
RainMon: an integrated approach to mining bursty timeseries monitoring data
Authors: Ilari Shafer, Kai Ren, Vishnu Naresh Boddeti, Yoshihisa Abe, Gregory R. Ganger, Christos Faloutsos
Venue: KDD
Year: 2012

Metrics like disk activity and network traffic are widespread sources of diagnosis and monitoring information in datacenters and networks. However, as the scale of these systems increases, examining the raw data yields diminishing insight. We present RainMon, a novel end-to-end approach for mining timeseries monitoring data designed to handle its size and unique characteristics. Our system is able to (a) mine large, bursty, real-world monitoring data, (b) find significant trends and anomalies in the data, (c) compress the raw data effectively, and (d) estimate trends to make forecasts. Furthermore, RainMon integrates the full analysis process from data storage to the user interface to provide accessible long-term diagnosis. We apply RainMon to three real-world datasets from production systems and show its utility in discovering anomalous machines and time periods.
EventSearch: a system for event discovery and retrieval on multi-type historical data
Authors: Dongdong Shan, Wayne Xin Zhao, Rishan Chen, Baihan Shu, Ziqi Wang, Junjie Yao, Hongfei Yan, Xiaoming Li
Venue: KDD
Year: 2012

We present EventSearch, a system for event extraction and retrieval on four types of news-related historical data, i.e., Web news articles, newspapers, TV news program, and micro-blog short messages. The system incorporates over 11 million web pages extracted from "Web InfoMall", the Chinese Web Archive since 2001. The newspaper and TV news video clips also span from 2001 to 2011. The system, upon a user query, returns a list of event snippets from multiple data sources. A novel burst model is used to discover events from time-stamped texts. In addition to offline event extraction, our system also provides online event extraction to further meet the user needs. EventSearch provides meaningful analytics that synthesize an accurate description of events. Users interact with the system by ranking the identified events using different criteria (scale, recency and relevance) and submitting their own information needs in different input fields.
Semi-supervised learning with mixed knowledge information
Authors: Fanhua Shang, Licheng Jiao, Fei Wang
Venue: KDD
Year: 2012

Integrating new knowledge sources into various learning tasks to improve their performance has recently become an interesting topic. In this paper we propose a novel semi-supervised learning (SSL) approach, called semi-supervised learning with Mixed Knowledge Information (SSL-MKI) which can simultaneously handle both sparse labeled data and additional pairwise constraints together with unlabeled data. Specifically, we first construct a unified SSL framework to combine the manifold assumption and the pairwise constraints assumption for classification tasks. Then we present a Modified Fixed Point Continuation (MFPC) algorithm with an eigenvalue thresholding (EVT) operator to learn the enhanced kernel matrix. Finally, we develop a two-stage optimization strategy and provide an efficient SSL approach that takes advantage of Laplacian spectral regularization: semi-supervised learning with Enhanced Spectral Kernel (ESK). Experimental results on a variety of synthetic and real-world datasets demonstrate the effectiveness of the proposed ESK approach.
Learning personal + social latent factor model for social recommendation
Authors: Yelong Shen, Ruoming Jin
Venue: KDD
Year: 2012

Social recommendation, which aims to systematically leverage the social relationships between users as well as their past behaviors for automatic recommendation, attract much attention recently. The belief is that users linked with each other in social networks tend to share certain common interests or have similar tastes (homophily principle); such similarity is expected to help improve the recommendation accuracy and quality. There have been a few studies on social recommendations; however, they almost completely ignored the heterogeneity and diversity of the social relationship.
LIEGE: : link entities in web lists with knowledge base
Authors: Wei Shen, Jianyong Wang, Ping Luo, Min Wang
Venue: KDD
Year: 2012

A critical step in bridging the knowledge base with the huge corpus of semi-structured Web list data is to link the entity mentions that appear in the Web lists with the corresponding real world entities in the knowledge base, which we call list linking task. This task can facilitate many different tasks such as knowledge base population, entity search and table annotation. However, the list linking task is challenging because a Web list has almost no textual context, and the only input for this task is a list of entity mentions extracted from the Web pages. In this paper, we propose LIEGE, the first general framework to Link the entities in web lists with the knowledge base to the best of our knowledge. Our assumption is that entities mentioned in a Web list can be any collection of entities that have the same conceptual type that people have in mind. To annotate the list items in a Web list with entities that they likely mention, we leverage the prior probability of an entity being mentioned and the global coherence between the types of entities in the Web list. The interdependence between different entity assignments in a Web list makes the optimization of this list linking problem NP-hard. Accordingly, we propose a practical solution based on the iterative substitution to jointly optimize the identification of the mapping entities for the Web list items. We extensively evaluated the performance of our proposed framework over both manually annotated real Web lists extracted from the Web pages and two public data sets, and the experimental results show that our framework significantly outperforms the baseline method in terms of accuracy.
China's national personal credit scoring system: a real-life intelligent knowledge application
Authors: Yong Shi
Venue: KDD
Year: 2012

Credit Reference Centre (CRC) of People's Bank of China (PBC) has built a big data: the largest personal credit database in the world with 800 million people's accounts collected from all commercial banks in China since 2003. From June 2006 to Sept 2009, Research Centre on Fictitious Economy and Data Science, Chinese Academy of Sciences (CASFEDS) and CRC jointly developed China's National Personal Credit Scoring System, known as "China Score", which is a unique and advanced KDD application under intelligent knowledge management on this big data. The system will be eventually serving all 1.3 billion population of China for their daily financial activities, such as bank accounts, credit card application, mortgage, personal loans, etc. It can become one of the most influential events of KDD techniques to human kind. This talk will introduce the key components of China Score project that includes objectives, modeling process, KDD techniques used in the projects, intelligent knowledge management and experience of the project development. In addition, the talk will also outline a number of policy recommendations based on China Score project which has been potentially impacting Chinese Government on its strategic decision making for China's economic developments.
Cross-media knowledge discovery
Authors: Zhongzhi Shi
Venue: KDD
Year: 2012

In this talk I introduce cloud computing based cross-media knowledge discovery. We propose a framework for cross-media semantic understanding which contains discriminative modeling, generative modeling and cognitive modeling. In cognitive modeling a new model entitled CAM is proposed which is suitable for cross-media semantic understanding. We develop an agent-aid model for load balance in cloud computing environment. For quality of service we present a utility function to evaluate the cloud performance. A Cross-Media Intelligent Retrieval System (CMIRS), which is managed by ontology-based knowledge system KMSphere, will be illustrated. Finally, the directions for further researches on cloud computing based cross-media knowledge discovery will be pointed out and discussed.
GetJar mobile application recommendations with very sparse datasets
Authors: Kent Shi, Kamal Ali
Venue: KDD
Year: 2012

The Netflix competition of 2006 [2] has spurred significant activity in the recommendations field, particularly in approaches using latent factor models [3,5,8,12] However, the near ubiquity of the Netflix and the similar MovieLens datasets1 may be narrowing the generality of lessons learned in this field. At GetJar, our goal is to make appealing recommendations of mobile applications (apps). For app usage, we observe a distribution that has higher kurtosis (heavier head and longer tail) than that for the aforementioned movie datasets. This happens primarily because of the large disparity in resources available to app developers and the low cost of app publication relative to movies.
HeteRecom: a semantic-based recommendation systemin heterogeneous networks
Authors: Chuan Shi, Chong Zhou, Xiangnan Kong, Philip S. Yu, Gang Liu, Bai Wang
Venue: KDD
Year: 2012

Making accurate recommendations for users has become an important function of e-commerce system with the rapid growth of WWW. Conventional recommendation systems usually recommend similar objects, which are of the same type with the query object without exploring the semantics of different similarity measures. In this paper, we organize objects in the recommendation system as a heterogeneous network. Through employing a path-based relevance measure to evaluate the relatedness between any-typed objects and capture the subtle semantic containing in each path, we implement a prototype system (called HeteRecom) for semantic based recommendation. HeteRecom has the following unique properties: (1) It provides the semantic-based recommendation function according to the path specified by users. (2) It recommends the similar objects of the same type as well as related objects of different types. We demonstrate the effectiveness of our system with a real-world movie data set.
Active learning for online bayesian matrix factorization
Authors: Jorge G. Silva, Lawrence Carin
Venue: KDD
Year: 2012

The problem of large-scale online matrix completion is addressed via a Bayesian approach. The proposed method learns a factor analysis (FA) model for large matrices, based on a small number of observed matrix elements, and leverages the statistical model to actively select which new matrix entries/observations would be most informative if they could be acquired, to improve the model; the model inference and active learning are performed in an online setting. In the context of online learning, a greedy, fast and provably near-optimal algorithm is employed to sequentially maximize the mutual information between past and future observations, taking advantage of submodularity properties. Additionally, a simpler procedure, which directly uses the posterior parameters learned by the Bayesian approach, is shown to achieve slightly lower estimation quality, with far less computational effort. Inference is performed using a computationally efficient online variational Bayes (VB) procedure. Competitive results are obtained in a very large collaborative filtering problem, namely the Yahoo! Music ratings dataset.
SympGraph: a framework for mining clinical notes through symptom relation graphs
Authors: Parikshit Sondhi, Jimeng Sun, Hanghang Tong, ChengXiang Zhai
Venue: KDD
Year: 2012

As an integral part of Electronic Health Records (EHRs), clinical notes pose special challenges for analyzing EHRs due to their unstructured nature. In this paper, we present a general mining framework SympGraph for modeling and analyzing symptom relationships in clinical notes.