Hongyun Cai

Near-duplicate video retrieval: Current research and future trends

The exponential growth of online videos, along with increasing user involvement in video-related activities, has been observed as a constant phenomenon during the last decade. Users time spent on video capturing, editing, uploading, searching, and viewing has boosted to an unprecedented level. The massive publishing and sharing of videos has given rise to the existence of an already large amount of near-duplicate content. This imposes urgent demands on near-duplicate video retrieval as a key role in novel tasks such as video search, video copyright protection, video recommendation, and many more. Driven by its significance, near-duplicate video retrieval has recently attracted a lot of attention. As discovered in recent works, latest improvements and progress in near-duplicate video retrieval, as well as related topics including low-level feature extraction, signature generation, and high-dimensional indexing, are employed to assist the process. As we survey the works in near-duplicate video retrieval, we comparatively investigate existing variants of the definition of near-duplicate video, describe a generic framework, summarize state-of-the-art practices, and explore the emerging trends in this research topic.

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications

Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximumly preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work addresses these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques, and application scenarios.

What are Popular: Exploring Twitter Features for Event Detection, Tracking and Visualization

As one of the most representative social media platforms, Twitter provides various real-life information on social events in real time. Despite that social event detection has been actively studied, tweet images, which appear in around 36 percent of the total tweets, have not been well utilized for this research problem. Most existing event detection methods tend to represent an image as a bag-of-visual-words and then process these visual words in the same way as textual words. This may not fully exploit the visual properties of images. State-of-the-art visual features like convolutional neural network (CNN) features have shown significant performance gains over the traditional bag-of-visual-words in unveiling the images semantics. Unfortunately, they have not been employed in detecting events from social websites. Hence, how to make the most of tweet images to improve the performance of social event detection and visualization remains open. In this paper, we thoroughly study the impact of tweet images on social event detection for different event categories using various visual features. A novel topic model which jointly models five Twitter features (text, image, location, timestamp and hashtag) is designed to discover events from the sheer amount of tweets. Moreover, the evolutions of events are tracked by linking the events detected on adjacent days and each event is visualized by representative images selected on three predefined criteria. Extensive experiments have been conducted on a real-life tweet dataset to verify the effectiveness of our method.

Learning Community Embedding with Community Detection and Node Embedding on Graphs

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.

Indexing Evolving Events from Tweet Streams

Tweet streams provide a variety of real-time information on dynamic social events. Although event detection has been actively studied, most of the existing approaches do not address the issue of efficient event monitoring in the presence of a large number of events detected from continuous tweet streams. In this paper, we capture the dynamics of events using four event operations: creation, absorption, split and merge.We also propose a novel event indexing structure, named Multi-layer Inverted List (MIL), for the acceleration of large-scale event search and update. We thoroughly study the problem of nearest neighbour search using MIL based on upper bound pruning. Extensive experiments have been conducted on a large-scale tweet dataset. The results demonstrate the promising performance of our method in terms of both efficiency and effectiveness.

From community detection to community profiling

Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.

Social event identification and ranking on flickr

Effective event modeling allows accurate event identification and monitoring to enable timely response to emergencies occurring in various applications. Although event identification (or detection) has been extensively studied in the last decade, the triggering relationship among initial and subsequent events has not been well studied, which limits the understanding of event evolvements from both spatial and temporal dimensions. Furthermore, it is also useful to measure the impact of events to the public so that the important events can be first seen. In this paper, we propose to systematically study event modeling and ranking in a novel framework. A new method is introduced to effectively identify events by considering the spreading effect of event in the spatio-temporal space. To capture the triggering relationships among events, we adapt the self-exciting point process model by jointly considering event spatial, temporal and content similarities. As a step further, we define the event impact and estimate it via random walk based on the triggering relationships. Finally, events can be ranked at different time stamps. Extensive experimental results on real-life datasets demonstrate promising performance of our proposal in identifying, monitoring and ranking events.

Indexing evolving events from tweet streams

Tweet streams provide a variety of real-life and real-time information on social events that dynamically change over time. Although social event detection has been actively studied, how to efficiently monitor evolving events from continuous tweet streams remains open and challenging. One common approach for event detection from text streams is to use single-pass incremental clustering. However, this approach does not track the evolution of events, nor does it address the issue of efficient monitoring in the presence of a large number of events. In this paper, we capture the dynamics of events using four event operations (create, absorb, split, and merge), which can be effectively used to monitor evolving events. Moreover, we propose a novel event indexing structure, called Multi-layer Inverted List (MIL), to manage dynamic event databases for the acceleration of large-scale event search and update. We thoroughly study the problem of nearest neighbour search using MIL based on upper bound pruning, along with incremental index maintenance. Extensive experiments have been conducted on a large-scale real-life tweet dataset. The results demonstrate the promising performance of our event indexing and monitoring methods on both efficiency and effectiveness.

EventEye: Monitoring Evolving Events from Tweet Streams

With the rapid growth in popularity of social websites, social event detection has become one of the hottest research topics. However, continuously monitoring social events has not been well studied. In this demo, we present a novel system called EventEye to effectively monitor evolving events and visualize their evolving paths, which are discovered from tweet streams. In particular, four event operations are defined for our proposed stream clustering algorithm to capture the evolutions over time and a multi-layer indexing structure is designed to support efficient event search from large-scale event databases. In our system, events are visualized in different views, including evolution graph, timeline, map view, etc.

Spatio-temporal Event Modeling and Ranking

Effective event modeling allows accurate event identification and monitoring to enable timely response to emergencies occurring in various applications. Although event identification has been extensively studied in the last decade, the triggering relationship among initial and subsequent events has not been well studied, which limits the understanding of event evolvements from both spatial and temporal dimensions. Furthermore, it is also useful to measure the impact of events to the public so that the important events can be first seen. In this paper, we propose to systematically study event modeling and ranking in a novel framework. A new method is introduced to effectively identify events by considering the spreading effect of event in the spatio-temporal space. To capture the triggering relationships among events, we adapt the self-exciting point process model by jointly considering event spatial, temporal and content similarities. As a step further, we define the event impact and rank them at different time stamps. Extensive experimental results on real-life datasets demonstrate promising performance of our proposal in identifying, monitoring and ranking events.