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Information diffusion, which addresses the issue of how a piece of information spreads and reaches individuals in or between networks, has attracted considerable research attention due to its widespread applications, such as viral marketing and ...

Discovering important papers in different academic topics is known as topic-sensitive influential paper discovery. Previous works mainly find the influential papers based on the structure of citation networks but neglect the text information, ...

Over the past 15 years, the average six-year graduation rates for colleges and universities across the Unites States have remained stable at around 60%. This vehemently impacts society in terms of workforce development, national productivity and ...

Social media and online news content are increasing rapidly. The goal of this work is to identify the topics associated with this content and understand the changing dynamics of these topics over time. We propose Topic Flow Model (TFM), a graph ...

Named Entity Recognition (NER) is a challenging task in Natural Language Processing. Recently, machine learning based methods are widely used for the NER task and outperform traditional handcrafted rule based methods. As an alternative way to ...

Regular expressions (regexes) are patterns that are used in many applications to extract words or tokens from text. However, even hand-crafted regexes may fail to match all the intended words. In this paper, we propose a novel way to generalize a ...

We explore the adoption of topic modeling to inform the seamless integration of community discovery and role analysis. For this purpose, we develop a new Bayesian probabilistic generative model of social media, according to which the observation ...

In this paper, we propose a model using generative adversarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative adversarial net. The use of high-level latent random ...

Effective reuse of existing crowdsourced intelligence present in Community Question Answering (CQA) forums requires efficient approaches for the problem of Duplicate Question Detection (DQD). Approaches which use standalone encoded representations ...

This paper designs a strategic model used to optimize click-though rates (CTR) for profitable recommendation systems. Approximating a function from samples as a vital step of data prediction is desirable when ground truth is not directly ...

Current quality control methods for crowdsourcing largely account for variations in worker responses to items by interactions between item difficulty and worker expertise. Few have taken into account the semantic relationships that can exist ...

Representation learning of knowledge graphs encodes entities and relation types into a continuous low-dimensional vector space, learns embeddings of entities and relation types. Most existing methods only concentrate on knowledge triples, ignoring ...

Recent successes in word embedding and document embedding have motivated researchers to explore similar representations for networks and to use such representations for tasks such as edge prediction, node label prediction, and community detection. ...

Network embeddings have become very popular in learning effective feature representations of networks. Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to ...

Network embedding has been a hot topic as it can learn node representations that encode the network structure resulting from node interactions. In this paper, besides the network structure, the interaction content within which each interaction ...

Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result, conventional network embedding techniques cannot work on such ...

User identification is widely used in anomaly detection, recommendation system and so on. Previous approaches focus on extraction of features describing users, and the learners try to emphasize the differences between different user identities. ...

Data sparsity is a common issue in recommendation systems, particularly collaborative filtering. In real recommendation scenarios, user preferences are often quantitatively sparse because of the application nature. To address the issue, we ...

Learning the low-dimensional representations of the vertices in a network can help users understand the network structure and perform other data mining tasks efficiently. Various network embedding approaches such as DeepWalk and LINE have been ...

Due to the ubiquity of graphs, machine learning on graphs facilitates many AI systems. In order to incorporate the rich information of graphs into machine learning models, graph embedding has been developed, which seeks to preserve the graphs into ...

This paper studies the problem of Representation Learning for network with textual information, which aims to learn low dimensional vectors for nodes by leveraging network structure and textual information. Most existing works only focus on one ...

Clinical Named Entity Recognition (CNER), the task of identifying the entity boundaries in clinical texts, is essential for many applications. Previous methods usually follow the traditional NER methods that heavily rely on language specific ...

Entity Set Expansion (ESE) is an important data mining task, e.g. query suggestion. It aims to expand an entity seed set to obtain more entities which have traits in common. Traditionally, text and Web information are widely used for ESE. Recently,...

We address the problem of identifying in-app user actions from Web access logs when the content of those logs is both encrypted (through HTTPS) and also contains automated Web accesses. We find that the distribution of time gaps between HTTPS ...

Recent efforts towards digitization of cultural heritage artifacts have resulted in a surge of information around these artifacts. However, the organization of these artifacts falls short with respect to accessing the facts across these entities. ...