OpenRaiser/Intern-Atlas huggingface dataset:
Streaming Link Prediction on Dynamic Attributed Networks
Authors: Jundong Li, Kewei Cheng, Liang Wu, Huan Liu
Venue: WSDM
Year: 2018

Link prediction targets to predict the future node interactions mainly based on the current network snapshot. It is a key step in understanding the formation and evolution of the underlying networks; and has practical implications in many real-world applications, ranging from friendship recommendation, click through prediction to targeted advertising. Most existing efforts are devoted to plain networks and assume the availability of network structure in memory before link prediction takes place. However, this assumption is untenable as many real-world networks are affiliated with rich node attributes, and often, the network structure and node attributes are both dynamically evolving at an unprecedented rate. Even though recent studies show that node attributes have an added value to network structure for accurate link prediction, it still remains a daunting task to support link prediction in an online fashion on such dynamic attributed networks. As changes in the dynamic attributed networks are often transient and can be endless, link prediction algorithms need to be efficient by making only one pass of the data with limited memory overhead. To tackle these challenges, we study a novel problem of streaming link prediction on dynamic attributed networks and present a novel framework - SLIDE. Methodologically, SLIDE maintains and updates a low-rank sketching matrix to summarize all observed data, and we further leverage the sketching matrix to infer missing links on the fly. The whole procedure is theoretically guaranteed, and empirical experiments on real-world dynamic attributed networks validate the effectiveness and efficiency of the proposed framework.
Bayesian Optimization for Optimizing Retrieval Systems
Authors: Dan Li, Evangelos Kanoulas
Venue: WSDM
Year: 2018

The effectiveness of information retrieval systems heavily depends on a large number of hyperparameters that need to be tuned. Hyperparameters range from the choice of different system components, e.g., stopword lists, stemming methods, or retrieval models, to model parameters, such as k1 and b in BM25, or the number of query expansion terms. Grid and random search, the dominant methods to search for the optimal system configuration, lack a search strategy that can guide them in the hyperparameter space. This makes them inefficient and ineffective. In this paper, we propose to use Bayesian Optimization to jointly search and optimize over the hyperparameter space. Bayesian Optimization, a sequential decision making method, suggests the next most promising configuration to be tested on the basis of the retrieval effectiveness of configurations that have been examined so far. To demonstrate the efficiency and effectiveness of Bayesian Optimization we conduct experiments on TREC collections, and show that Bayesian Optimization outperforms manual tuning, grid search and random search, both in terms of retrieval effectiveness of the configuration found, and in terms of efficiency in finding this configuration.
Network Science of Teams: Characterization, Prediction, and Optimization
Authors: Liangyue Li, Hanghang Tong
Venue: WSDM
Year: 2018

Teams are increasingly indispensable to achievements in any organization. Despite the organizations' substantial dependency on teams, fundamental knowledge about the conduct of team-enabled operations is lacking, especially at the social, cognitive and information level in relation to team performance and network dynamics.
The 5th International Workshop on Social Web for Disaster Management(SWDM'18): Collective Sensing, Trust, and Resilience in Global Crises
Authors: Yu-Ru Lin, Carlos Castillo, Jie Yin
Venue: WSDM
Year: 2018

During large-scale emergencies such as natural and man-made disasters, a massive amount of information is posted by the public in social media. Collecting, aggregating, and presenting this information to stakeholders can be extremely challenging, particularly if an understanding of the "big picture»» is sought. This international workshop, the fifth in the series, is a key venue for researchers and practitioners to discuss research challenges and technical issues around the usage of social media in disaster management. Workshop»s website: https://sites.google.com/site/swdm2018/
Event Mining over Distributed Text Streams
Authors: John Calvo Martinez
Venue: WSDM
Year: 2018

This research presents a new set of techniques to deal with event mining from different text sources, a complex set of NLP tasks which aim to extract events of interest and their components including authors, targets, locations, and event categories. Our focus is on distributed text streams, such as tweets from different news agencies, in order to accurately retrieve events and its components by combining such sources in different ways using text stream mining. Therefore this research project aims to fill the gap between batch event mining, text stream mining and distributed data mining which have been used separately to address related learning tasks. We propose a multi-task and multi-stream mining approach to combine information from multiple text streams to accurately extract and categorise events under the assumptions of stream mining. Our approach also combines ontology matching to boost accuracy under imbalanced distributions. In addition, we plan to address two relatively unexplored event mining tasks: event coreference and event synthesis. Preliminary results show the appropriateness of our proposal, which is giving an increase of around 20% on macro prequential metrics for the event classification task.
LearnIR: WSDM 2018 Workshop on Learning from User Interactions
Authors: Rishabh Mehrotra, Ahmed Hassan Awadallah, Emine Yilmaz
Venue: WSDM
Year: 2018

While users interact with online services(e.g. search engines, recommender systems, conversational agents), they leave behind fine grained traces of interaction patterns. The ability to understand user behavior, record and interpret user interaction signals, gauge user satisfaction and incorporate user feedback gives online systems a vast treasure trove of insights for improvement and experimentation. More generally, the ability to learn from user interactions promises pathways for solving a number of problems and improving user engagement and satisfaction.
Index Compression Using Byte-Aligned ANS Coding and Two-Dimensional Contexts
Authors: Alistair Moffat, Matthias Petri
Venue: WSDM
Year: 2018

We examine approaches used for block-based inverted index compression, such as the OptPFOR mechanism, in which fixed-length blocks of postings data are compressed independently of each other. Building on previous work in which asymmetric numeral systems (ANS) entropy coding is used to represent each block, we explore a number of enhancements: (i) the use of two-dimensional conditioning contexts, with two aggregate parameters used in each block to categorize the distribution of symbol values that underlies the ANS approach, rather than just one; (ii) the use of a byte-friendly strategic mapping from symbols to ANS codeword buckets; and (iii) the use of a context merging process to combine similar probability distributions. Collectively, these improvements yield superior compression for index data, outperforming the reference point set by the Interp mechanism, and hence representing a significant step forward. We describe experiments using the 426 GiB gov2 collection and a new large collection of publicly-available news articles to demonstrate that claim, and provide query evaluation throughput rates compared to other block-based mechanisms.
Fusing Diversity in Recommendations in Heterogeneous Information Networks
Authors: Sharad Nandanwar, Aayush Moroney, M. Narasimha Murty
Venue: WSDM
Year: 2018

In the past, hybrid recommender systems have shown the power of exploiting relationships amongst objects which directly or indirectly effect the recommendation task. However, the effect of all relations is not equal, and choosing their right balance for a recommendation problem at hand is non-trivial. We model these interactions using a Heterogeneous Information Network, and propose a systematic framework for learning their influence weights for a given recommendation task. Further, we address the issue of redundant results, which is very much prevalent in recommender systems. To alleviate redundancy in recommendations we use Vertex Reinforced Random Walk (a non-Markovian random walk) over a heterogeneous graph. It works by boosting the transitions to the influential nodes, while simultaneously shrinking the weights of others. This helps in discouraging recommendation of multiple influential nodes which lie in close proximity of each other, thus ensuring diversity. Finally, we demonstrate the effectiveness of our approach by experimenting on real world datasets. We find that, with the weights of relations learned using the proposed non-Markovian random walk based framework, the results consistently improve over the baselines.
Beyond Who and What: Data Driven Approaches for User Characterization
Authors: Aastha Nigam
Venue: WSDM
Year: 2018

Social media and technology have drastically transformed the social and information networks around us. They have impacted how we communicate with others, search for information, and even how we express our personal opinions. Further, in this era of big data, not only are the online services collecting vast variety of user data, but we, as users, are also readily divulging significant amounts of information. Together, massive datasets obtained from diverse sources such as organizations and user generated content give us the opportunity to explore and understand complex behavior of both individuals and communities. This proposal aims at designing generalizable and scalable data-driven frameworks to gain a deeper understanding of the users, explain their actions and preferences, and infer personal traits. The proposed models will enable us to move beyond asking the conventional questions of who and what, and reveal answers about how and why. Given the varying digital persona of users motivated by their personal preferences and social attributes, we characterize users in two distinct domains: online health and peace studies. The models are designed to solve various real-world challenges to maximize their broader impact.
Athlytics: Winning in Sports with Data
Authors: Konstantinos Pelechrinis, Evangelos E. Papalexakis
Venue: WSDM
Year: 2018

Data and analytics have been part of the sports industry from as early as the 1870s, when the first boxscore in baseball was recorded. However, it is only recently that advanced data mining and machine learning techniques have been utilized for facilitating the operations of sports franchises. While part of the reason is related with the ability to collect more fine-grained data, an equally important factor for this turn to analytics is the huge success and competitive advantage that early adopters of investment in analytics enjoyed(popularized by the best-seller -Moneyball? that described the success that Oakland Athletics had with analytics). Draft selection, game-day decision making and player evaluation are just a few of the applications where sports analytics play a crucial role today. Apart from the sports clubs, other stakeholders in the industry(e.g., the leagues' offices, media, etc.) invest in analytics. The leagues increasingly rely on data in order to decide on potential rule changes. For instance, the most recent rule change in NFL, i.e., the kickoff touchback, was a result of thorough data analysis of concussion instances. In this tutorial we will review the literature in data mining and machine learning techniques for sports analytics. We will introduce the audience to the design and methodologies behind advanced metrics such as the adjusted plus/minus for evaluating basketball players, spatial metrics for evaluating the ability of a player to spread the defense in basketball, and the Player Efficiency Rating(PER). We will also go in depth in advanced data mining methods, and in particular tensor mining, that can analyze heterogenous data similar to the ones available in today's sports world.
Conversations, Machine Learning and Privacy: LinkedIn's Path Towards Transforming Interaction with Its Members
Authors: Igor Perisic
Venue: WSDM
Year: 2018

At LinkedIn, we believe that having the right conversations with our members is key to unlocking economic opportunity for them. For us, these conversations are in a broader context than traditionally defined dialogues. A typical dialogue usually only considers a limited time-window as context and is trying to satisfy an immediate intent. Advanced dialogue systems allow an user to take a number of turns, in that short-time window, to get clear on the user's intent. However, our members are having conversations with us over long periods of time about their long-term goals, such as staying informed, growing a professional network, advancing a career, getting a job, finding qualified leads, etc. These conversational goals are often hierarchical. For example, getting a great job is a key part of advancing your career. Our goal at LinkedIn is to be able to have simultaneous conversations with our members on all of these levels. To do this, we have to build machine learning systems that understand that there are multiple multi-level conversations going on. We have made strong headway in building components of this conversational vision by learning how to approximate long-term member value and defining an optimization framework that can incorporate multiple conflicting objectives. These problems consider the states of these conversations when interacting with our members and actively make decisions that optimize this ongoing dialogue. We have a challenging and interesting road ahead. In this talk, Igor will present the current state of LinkedIn's machine-learning efforts towards building robust, long-term conversational systems. He will then discuss the potential privacy and ethical issues surrounding having these conversational interactions through an ever-increasing number of touchpoints with our members.
Extreme Multi-label Learning with Label Features for Warm-start Tagging, Ranking & Recommendation
Authors: Yashoteja Prabhu, Anil Kag, Shilpa Gopinath, Kunal Dahiya, Shrutendra Harsola, Rahul Agrawal, Manik Varma
Venue: WSDM
Year: 2018

The objective in extreme multi-label learning is to build classifiers that can annotate a data point with the subset of relevant labels from an extremely large label set. Extreme classification has, thus far, only been studied in the context of predicting labels for novel test points. This paper formulates the extreme classification problem when predictions need to be made on training points with partially revealed labels. This allows the reformulation of warm-start tagging, ranking and recommendation problems as extreme multi-label learning with each item to be ranked/recommended being mapped onto a separate label. The SwiftXML algorithm is developed to tackle such warm-start applications by leveraging label features. SwiftXML improves upon the state-of-the-art tree based extreme classifiers by partitioning tree nodes using two hyperplanes learnt jointly in the label and data point feature spaces. Optimization is carried out via an alternating minimization algorithm allowing SwiftXML to efficiently scale to large problems.
Mining Knowledge Graphs From Text
Authors: Jay Pujara, Sameer Singh
Venue: WSDM
Year: 2018

Knowledge graphs have become an increasingly crucial component in machine intelligence systems, powering ubiquitous digital assistants and inspiring several large scale academic projects across the globe. Our tutorial explains why knowledge graphs are important, how knowledge graphs are constructed, and where new research opportunities exist for improving the state-of-the-art. In this tutorial, we cover the many sophisticated approaches that complete and correct knowledge graphs. We organize this exploration into two main classes of models. The first include probabilistic logical frameworks that use graphical models, random walks, or statistical rule mining to construct knowledge graphs. The second class of models includes latent space models such as matrix and tensor factorization and neural networks. We conclude the tutorial with a critical comparison of techniques and results. We will offer practical advice for novices to identify common empirical challenges and concrete data sets for initial experimentation. Finally, we will highlight promising areas of current and future work.
DSANLS: Accelerating Distributed Nonnegative Matrix Factorization via Sketching
Authors: Yuqiu Qian, Conghui Tan, Nikos Mamoulis, David W. Cheung
Venue: WSDM
Year: 2018

Nonnegative matrix factorization (NMF) has been successfully applied in different fields, such as text mining, image processing, and video analysis. NMF is the problem of determining two nonnegative low rank matrices U and V, for a given input matrix M, such that m ≈ UV⊥. There is an increasing interest in parallel and distributed NMF algorithms, due to the high cost of centralized NMF on large matrices. In this paper, we propose a distributed sketched alternating nonnegative least squares(DSANLS) framework for NMF, which utilizes a matrix sketching technique to reduce the size of nonnegative least squares subproblems in each iteration for U and V. We design and analyze two different random matrix generation techniques and two subproblem solvers. Our theoretical analysis shows that DSANLS converges to the stationary point of the original NMF problem and it greatly reduces the computational cost in each subproblem as well as the communication cost within the cluster. DSANLS is implemented using MPI for communication, and tested on both dense and sparse real datasets. The results demonstrate the efficiency and scalability of our framework, compared to the state-of-art distributed NMF MPI implementation.
Engagement and Incentives in Online Community: Observational Data, Prediction Models, and Field Experiments
Authors: Jiezhong Qiu
Venue: WSDM
Year: 2018

This proposal aims to study user engagement pattern and how different incentive mechanisms influence user behavior in online communities. Work in this proposal investigates the diverse behavior patterns that different individuals follow in various online communities, and how incentive design can help increase user engagement. First, our work on MOOCs leads to the discovery of behavioral heterogeneity in students course selection as well as their learning patterns. Secondly, our work on social messaging groups characterizes the formation and evolution pattern of chat groups regarding their lifecycles, structures dynamics, and underlying diffusion processes. Finally, we design and deploy a large-scale online experiment to explore how social tie, as a type of incentive, can help call back dropout users in a social game community. To the end, studying engagement and incentive offers us an opportunity to understand the fundamental principles that drive our online behaviors and activities - from individuals, to groups, to communities - and, in this way, to help design and build better online communities and organizations.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
Authors: Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang
Venue: WSDM
Year: 2018

Since the invention of word2vec, the skip-gram model has significantly advanced the research of network embedding, such as the recent emergence of the DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms. Our analysis and proofs reveal that: (1) DeepWalk empirically produces a low-rank transformation of a network's normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk when the size of vertices' context is set to one; (3) As an extension of LINE, PTE can be viewed as the joint factorization of multiple networks» Laplacians; (4) node2vec is factorizing a matrix related to the stationary distribution and transition probability tensor of a 2nd-order random walk. We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. Finally, we present the NetMF method as well as its approximation algorithm for computing network embedding. Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks. This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning
Authors: Meng Qu, Jian Tang, Jiawei Han
Venue: WSDM
Year: 2018

Learning node representations for networks has attracted much attention recently due to its effectiveness in a variety of applications. This paper focuses on learning node representations for heterogeneous star networks, which have a center node type linked with multiple attribute node types through different types of edges. In heterogeneous star networks, we observe that the training order of different types of edges affects the learning performance significantly. Therefore we study learning curricula for node representation learning in heterogeneous star networks, i.e., learning an optimal sequence of edges of different types for the node representation learning process. We formulate the problem as a Markov decision process, with the action as selecting a specific type of edges for learning or terminating the training process, and the state as the sequence of edge types selected so far. The reward is calculated as the performance on external tasks with node representations as features, and the goal is to take a series of actions to maximize the cumulative rewards. We propose an approach based on deep reinforcement learning for this problem. Our approach leverages LSTM models to encode states and further estimate the expected cumulative reward of each state-action pair, which essentially measures the long-term performance of different actions at each state. Experimental results on real-world heterogeneous star networks demonstrate the effectiveness and efficiency of our approach over competitive baseline approaches.
Leveraging Implicit Contribution Amounts to Facilitate Microfinancing Requests
Authors: Suhas Ranganath, Ghazaleh Beigi, Huan Liu
Venue: WSDM
Year: 2018

The emergence of online microfinancing platforms provides new opportunities for people to seek financial assistance from a large number of potential contributors. However, these platforms deal with a huge number of requests, making it hard for the requesters to get assistance for their financial needs. Designing algorithms to identify potential contributors for a given request will assist in satisfying financial needs of requesters and improve the effectiveness of microfinancing platforms. Existing work correlates requests with contributor interests and profiles to design feature based approaches for recommending projects to prospective contributors. However, contributing money to financial requests has a cost on contributors which can affect his inclination to contribute in the future . Literature in economic behavior has investigated the manner in which memory of past contribution amounts affects user inclination to contribute to a given request. To systematically investigate whether these characteristics of economic behavior would help to facilitate requests in online microfinancing platforms, we present a novel framework to identify contributors for a given request from their past financial information. Individual contribution amounts are not publicly available, so we draw from financial modeling literature to model the implicit contribution amounts made to past requests. We evaluate the framework on two microfinancing platforms to demonstrate its effectiveness in identifying contributors.