OpenRaiser/Intern-Atlas huggingface dataset:
Non-transitive Hashing with Latent Similarity Components
Authors: Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu
Venue: Conference KDD 2015
Year: 2015

Approximating the semantic similarity between entities in the learned Hamming space is the key for supervised hashing techniques. The semantic similarities between entities are often non-transitive since they could share different latent similarity components. For example, in social networks, we connect with people for various reasons, such as sharing common interests, working in the same company, being alumni and so on. Obviously, these social connections are non-transitive if people are connected due to different reasons. However, existing supervised hashing methods treat the pairwise similarity relationships in a simple and unified way and project data into a single Hamming space, while neglecting that the non-transitive property cannot be ade- quately captured by a single Hamming space. In this paper, we propose a non-transitive hashing method, namely Multi-Component Hashing (MuCH), to identify the latent similarity components to cope with the non-transitive similarity relationships. MuCH generates multiple hash tables with each hash table corresponding to a similarity component, and preserves the non-transitive similarities in different hash table respectively. Moreover, we propose a similarity measure, called Multi-Component Similarity, aggregating Hamming similarities in multiple hash tables to capture the non-transitive property of semantic similarity. We conduct extensive experiments on one synthetic dataset and two public real-world datasets (i.e. DBLP and NUS-WIDE). The results clearly demonstrate that the proposed MuCH method significantly outperforms the state-of-art hashing methods especially on search efficiency.
A supervised learning framework for prediction of incompatible herb pair in traditional chinese medicine
Authors: Jiajing Zhu, Yongguo Liu, Shangming Yang, Shuangqing Zhai, Zhang Yi, Chuanbiao Wen
Venue: Conference CIKM 2018
Year: 2018

Adverse drug-drug interaction has been a critical issue for the development of drugs. In Traditional Chinese Medicine, adverse herb-herb interaction is a negative reaction in patients after the absorption of decoction of Incompatible Herb Pair (IHP). Recently, many methods have been proposed for IHP research, but most of them focused on revealing and analyzing the adverse reaction of some known IHPs, despite that a number of new IHPs have been discovered by accidents. Up to now, IHPs have been a serious threat to public health in the TCM medication. In this paper, we propose a novel supervised learning framework for potential IHP prediction. In this framework, we model the prediction task as a non-negative matrix tri-factorization problem, in which two important herb attributes (efficacy and flavor) and their correlation are incorporated to characterize the incompatible relationship among herbs. A hypothetical test method is adopted to evaluate the statistical significance of dissimilar characteristics of two attributes and the results are used as a regularization term to improve the accuracy of IHP prediction. Experiments on the real-world IHP dataset demonstrate that the proposed framework is very effective for prediction of potential IHPs.
Learning community embedding with community detection and node embedding on graphs
Authors: Sandro Cavallari, Vincent W. Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria
Venue: Conference CIKM 2017
Year: 2017

In this paper, we study an important yet largely under-explored setting of graph embedding, i.e., embedding communities instead of each individual nodes. We find that community embedding is not only useful for community-level applications such as graph visualization, but also beneficial to both community detection and node classification. To learn such embedding, our insight hinges upon a closed loop among community embedding, community detection and node embedding. On the one hand, node embedding can help improve community detection, which outputs good communities for fitting better community embedding. On the other hand, community embedding can be used to optimize the node embedding by introducing a community-aware high-order proximity. Guided by this insight, we propose a novel community embedding framework that jointly solves the three tasks together. We evaluate such a framework on multiple real-world datasets, and show that it improves graph visualization and outperforms state-of-the-art baselines in various application tasks, e.g., community detection and node classification.
Dynamics of large multi-view social networks: Synergy, cannibalization and cross-view interplay
Authors: Yu Shi, Myunghwan Kim, Shaunak Chatterjee, Mitul Tiwari, Souvik Ghosh, Rómer Rosales
Venue: Conference KDD 2016
Year: 2016

Most social networking services support multiple types of relationships between users, such as getting connected, sending messages, and consuming feed updates. These users and relationships can be naturally represented as a dynamic multi-view network, which is a set of weighted graphs with shared common nodes but having their own respective edges. Different network views, representing structural relationship and interaction types, could have very distinctive properties individually and these properties may change due to interplay across views. Therefore, it is of interest to study how multiple views interact and affect network dynamics and, in addition, explore possible applications to social networking. In this paper, we propose approaches to capture and analyze multi-view network dynamics from various aspects. Through our proposed descriptors, we observe the synergy and cannibalization between different user groups and network views from LinkedIn dataset. We then develop models that consider the synergy and cannibalization per new relationship, and show the outperforming predictive capability of our models compared to baseline models. Finally, the proposed models allow us to understand the interplay among different views where they dynamically change over time.
Semantic hierarchies for visual object recognition
Authors: Marcin Marszalek, Cordelia Schmid
Venue: Conference CVPR 2007
Year: 2007

In this paper we propose to use lexical semantic networks to extend the state-of-the-art object recognition techniques. We use the semantics of image labels to integrate prior knowledge about inter-class relationships into the visual appearance learning. We show how to build and train a semantic hierarchy of discriminative classifiers and how to use it to perform object detection. We evaluate how our approach influences the classification accuracy and speed on the PASCAL VOC challenge 2006 dataset, a set of challenging real-world images. We also demonstrate additional features that become available to object recognition due to the extension with semantic inference toolswe can classify high-level categories, such as animals, and we can train part detectors, for example a window detector, by pure inference in the semantic network.
PME: projected metric embedding on heterogeneous networks for link prediction
Authors: Hongxu Chen, Hongzhi Yin, Weiqing Wang, Hao Wang, Quoc Viet Hung Nguyen, Xue Li
Venue: Conference KDD 2018
Year: 2018

Heterogenous information network embedding aims to embed heterogenous information networks (HINs) into low dimensional spaces, in which each vertex is represented as a low-dimensional vector, and both global and local network structures in the original space are preserved. However, most of existing heterogenous information network embedding models adopt the dot product to measure the proximity in the low dimensional space, and thus they can only preserve the first-order proximity and are insufficient to capture the global structure. Compared with homogenous information networks, there are multiple types of links (i.e., multiple relations) in HINs, and the link distribution w.r.t relations is highly skewed. To address the above challenging issues, we propose a novel heterogenous information network embedding model PME based on the metric learning to capture both first-order and second-order proximities in a unified way. To alleviate the potential geometrical inflexibility of existing metric learning approaches, we propose to build object and relation embeddings in separate object space and relation spaces rather than in a common space. Afterwards, we learn embeddings by firstly projecting vertices from object space to corresponding relation space and then calculate the proximity between projected vertices. To overcome the heavy skewness of the link distribution w.r.t relations and avoid "over-sampling'' or "under-sampling'' for each relation, we propose a novel loss-aware adaptive sampling approach for the model optimization. Extensive experiments have been conducted on a large-scale HIN dataset, and the experimental results show superiority of our proposed PME model in terms of prediction accuracy and scalability.
Gotcha - Sly Malware!: Scorpion A Metagraph2vec Based Malware Detection System
Authors: Yujie Fan, Shifu Hou, Yiming Zhang, Yanfang Ye, Melih Abdulhayoglu
Venue: Conference KDD 2018
Year: 2018

Due to its severe damages and threats to the security of the Internet and computing devices, malware detection has caught the attention of both anti-malware industry and researchers for decades. To combat the evolving malware attacks, in this paper, we first study how to utilize both content- and relation-based features to characterize sly malware; to model different types of entities (i.e., file, archive, machine, API, DLL ) and the rich semantic relationships among them (i.e., file-archive, file-machine, file-file, API-DLL, file-API relations), we then construct a structural heterogeneous information network (HIN) and present meta-graph based approach to depict the relatedness over files. To measure the relatedness over files on the constructed HIN, since malware detection is a cost-sensitive task, it calls for efficient methods to learn latent representations for HIN. To address this challenge, based on the built meta-graph schemes, we propose a new HIN embedding model metagraph2vec on the first attempt to learn the low-dimensional representations for the nodes in HIN, where both the HIN structures and semantics are maximally preserved for malware detection. A comprehensive experimental study on the real sample collections from Comodo Cloud Security Center is performed to compare various malware detection approaches. The promising experimental results demonstrate that our developed system Scorpion which integrate our proposed method outperforms other alternative malware detection techniques. The developed system has already been incorporated into the scanning tool of Comodo Antivirus product.
Community Detection in Graphs Through Correlation
Authors: Lian Duan, William Nick Street, Yanchi Liu, Haibing Lu
Venue: Conference KDD 2014
Year: 2014

Community detection is an important task for social networks, which helps us understand the functional modules on the whole network. Among different community detection methods based on graph structures, modularity-based methods are very popular recently, but suffer a well-known resolution limit problem. This paper connects modularity-based methods with correlation analysis by subtly reformatting their math formulas and investigates how to fully make use of correlation analysis to change the objective function of modularity-based methods, which provides a more natural and effective way to solve the resolution limit problem. In addition, a novel theoretical analysis on the upper bound of different objective functions helps us understand their bias to different community sizes, and experiments are conducted on both real life and simulated data to validate our findings.
FORank: Fast ObjectRank for Large Heterogeneous Graphs
Authors: Tomoki Sato, Hiroaki Shiokawa, Yuto Yamaguchi, Hiroyuki Kitagawa
Venue: Conference WWW 2018
Year: 2018

ObjectRank is one of the popular graph mining methods that enables us to evaluate the importance of each vertex on heterogeneous graphs. However, it is computationally expensive to apply it to large graphs since ObjectRank needs to compute the importance of all vertices iteratively. In this work, we present a fast ObjectRank algorithm,FORank, that accurately approximates the keyword search results. FORank iteratively prunes vertices whose convergence score likely has less impact on the results during iterative computation. The experiments showed that FORank runs 7 times faster than ObjectRank computation with over 90% accuracy approximation.
Scalable and near real-time burst detection from ecommerce queries
Authors: Nish Parikh, Neel Sundaresan
Venue: Conference KDD 2008
Year: 2008

In large scale online systems like Search, eCommerce, or social network applications, user queries represent an important dimension of activities that can be used to study the impact on the system, and even the business. In this paper, we describe how to detect, characterize and classify bursts in user queries in a large scale eCommerce system. We build upon the approaches discussed in KDD 2002 "Bursty and Hierarchical Structure in Streams" [3] and apply them to a high volume industrial context. We describe how to identify bursts on a near real-time basis, classify them, and apply them to build interesting merchandizing applications.
Deep computational phenotyping
Authors: Zhengping Che, David C. Kale, Wenzhe Li, Mohammad Taha Bahadori, Yan Liu
Venue: Conference KDD 2015
Year: 2015

We apply deep learning to the problem of discovery and detection of characteristic patterns of physiology in clinical time series data. We propose two novel modifications to standard neural net training that address challenges and exploit properties that are peculiar, if not exclusive, to medical data. First, we examine a general framework for using prior knowledge to regularize parameters in the topmost layers. This framework can leverage priors of any form, ranging from formal ontologies (e.g., ICD9 codes) to data-derived similarity. Second, we describe a scalable procedure for training a collection of neural networks of different sizes but with partially shared architectures. Both of these innovations are well-suited to medical applications, where available data are not yet Internet scale and have many sparse outputs (e.g., rare diagnoses) but which have exploitable structure (e.g., temporal order and relationships between labels). However, both techniques are sufficiently general to be applied to other problems and domains. We demonstrate the empirical efficacy of both techniques on two real-world hospital data sets and show that the resulting neural nets learn interpretable and clinically relevant features.
Esdrank: Connecting query and documents through external semi-structured data
Authors: Chenyan Xiong, Jamie Callan
Venue: Conference CIKM 2015
Year: 2015

This paper presents EsdRank, a new technique for improving ranking using external semi-structured data such as controlled vocabularies and knowledge bases. EsdRank treats vocabularies, terms and entities from external data, as objects connecting query and documents. Evidence used to link query to objects, and to rank documents are incorporated as features between query-object and object-document correspondingly. A latent listwise learning to rank algorithm, Latent-ListMLE, models the objects as latent space between query and documents, and learns how to handle all evidence in a unified procedure from document relevance judgments. EsdRank is tested in two scenarios: Using a knowledge base for web search, and using a controlled vocabulary for medical search. Experiments on TREC Web Track and OHSUMED data show significant improvements over state-of-the-art baselines.
Effective music feature ncp: Enhancing cover song recognition with music transcription
Authors: Yao Cheng, Xiaoou Chen, Deshun Yang, Xiaoshuo Xu
Venue: Conference SIGIR 2017
Year: 2017

Chroma is a widespread feature for cover song recognition, as it is robust against non-tonal components and independent of timbre and specific instruments. However, Chroma is derived from spectrogram, thus it provides a coarse approximation representation of musical score. In this paper, we proposed a similar but more effective feature Note Class Profile (NCP) derived with music transcription techniques. NCP is a multi-dimensional time serie, each column of which denotes the energy distribution of 12 note classes. Experimental results on benchmark datasets demonstrated its superior performance over existing music features. In addition, NCP feature can be enhanced further with the development of music transcription techniques. The source code can be found in github1.
A characterization of online browsing behavior
Authors: Ravi Kumar 0001, Andrew Tomkins
Venue: Conference WWW 2010
Year: 2010

In this paper, we undertake a large-scale study of online user behavior based on search and toolbar logs. We propose a new CCS taxonomy of pageviews consisting of Content (news, portals, games, verticals, multimedia), Communication (email, social networking, forums, blogs, chat), and Search (Web search, item search, multimedia search). We show that roughly half of all pageviews online are content, one-third are communications, and the remaining one-sixth are search. We then give further breakdowns to characterize the pageviews within each high-level category.
Are spatial and global constraints really necessary for segmentation?
Authors: Aurélien Lucchi, Yunpeng Li 0002, Xavier Boix Bosch, Kevin Smith 0001, Pascal Fua
Venue: Conference ICCV 2011
Year: 2011

Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accomplished by introducing additional latent variables to the model, which can greatly increase its complexity. As a result, estimating the model parameters or computing the best maximum a posteriori (MAP) assignment becomes a computationally expensive task. In a series of experiments on the PASCAL and the MSRC datasets, we were unable to find evidence of a significant performance increase attributed to the introduction of such constraints. On the contrary, we found that similar levels of performance can be achieved using a much simpler design that essentially ignores these constraints. This more simple approach makes use of the same local and global features to leverage evidence from the image, but instead directly biases the preferences of individual pixels. While our investigation does not prove that spatial and consistency constraints are not useful in principle, it points to the conclusion that they should be validated in a larger context.
Xbully: Cyberbullying detection within a multi-modal context
Authors: Lu Cheng, Jundong Li, Yasin N. Silva, Deborah L. Hall, Huan Liu
Venue: Conference WSDM 2019
Year: 2019

Over the last decade, research has revealed the high prevalence of cyberbullying among youth and raised serious concerns in society. Information on the social media platforms where cyberbullying is most prevalent (e.g., Instagram, Facebook, Twitter) is inherently multi-modal, yet most existing work on cyberbullying identification has focused solely on building generic classification models that rely exclusively on text analysis of online social media sessions (e.g., posts). Despite their empirical success, these efforts ignore the multi-modal information manifested in social media data (e.g., image, video, user profile, time, and location), and thus fail to offer a comprehensive understanding of cyberbullying. Conventionally, when information from different modalities is presented together, it often reveals complementary insights about the application domain and facilitates better learning performance. In this paper, we study the novel problem of cyberbullying detection within a multi-modal context by exploiting social media data in a collaborative way. This task, however, is challenging due to the complex combination of both cross-modal correlations among various modalities and structural dependencies between different social media sessions, and the diverse attribute information of different modalities. To address these challenges, we propose XBully, a novel cyberbullying detection framework, that first reformulates multi-modal social media data as a heterogeneous network and then aims to learn node embedding representations upon it. Extensive experimental evaluations on real-world multi-modal social media datasets show that the XBully framework is superior to the state-of-the-art cyberbullying detection models.
Cohort modeling for enhanced personalized search
Authors: Jinyun Yan, Wei Chu, Ryen W. White
Venue: Conference SIGIR 2014
Year: 2014

Web search engines utilize behavioral signals to develop search experiences tailored to individual users. To be effective, such personalization relies on access to sufficient information about each user's interests and intentions. For new users or new queries, profile information may be sparse or non-existent. To handle these cases, and perhaps also improve personalization for those with profiles, search engines can employ signals from users who are similar along one or more dimensions, i.e., those in the same cohort. In this paper we describe a characterization and evaluation of the use of such cohort modeling to enhance search personalization. We experiment with three pre-defined cohorts-topic, location, and top-level domain preference-independently and in combination, and also evaluate methods to learn cohorts dynamically. We show via extensive experimentation with large-scale logs from a commercial search engine that leveraging cohort behavior can yield significant relevance gains when combined with a production search engine ranking algorithm that uses similar classes of personalization signal but at the individual searcher level. Additional experiments show that our gains can be extended when we dynamically learn cohorts and target easily-identifiable classes of ambiguous or unseen queries.
Combating crowdsourced review manipulators: A neighborhood-based approach
Authors: Parisa Kaghazgaran, James Caverlee, Anna Cinzia Squicciarini
Venue: Conference WSDM 2018
Year: 2018

We propose a system called TwoFace to uncover crowdsourced review manipulators who target online review systems. A unique feature of TwoFace is its three-phase framework: (i) in the first phase, we intelligently sample actual evidence of manipulation (e.g., review manipulators) by exploiting low moderation crowdsourcing platforms that reveal evidence of strategic manipulation; (ii) we then propagate the suspiciousness of these seed users to identify similar users through a random walk over a "suspiciousness" graph; and (iii) finally, we uncover (hidden) distant users who serve structurally similar roles by mapping users into a low-dimensional embedding space that captures community structure. Altogether, the TwoFace system recovers 83% to 93% of all manipulators in a sample from Amazon of 38,590 reviewers, even when the system is seeded with only a few samples from malicious crowdsourcing sites.
Exploiting web search to generate synonyms for entities
Authors: Surajit Chaudhuri, Venkatesh Ganti, Dong Xin
Venue: Conference WWW 2009
Year: 2009

Tasks recognizing named entities such as products, people names, or locations from documents have recently received significant attention in the literature. Many solutions to these tasks assume the existence of reference entity tables. An important challenge that needs to be addressed in the entity extraction task is that of ascertaining whether or not a candidate string approximately matches with a named entity in a given reference table.