Abstract Real‐world event sequences are often complex and heterogeneous, making it difficult to create meaningful visualizations using simple data aggregation and visual encoding techniques. Consequently, visualization researchers have developed numerous visual summarization techniques to generate concise overviews of sequential data. These techniques vary widely in terms of summary structures and contents, and currently there is a knowledge gap in understanding the effectiveness of these techniques. In this work, we present the design and results of an insight‐based crowdsourcing experiment evaluating three existing visual summarization techniques: CoreFlow, SentenTree, and Sequence Synopsis. We compare the visual summaries generated by these techniques across three tasks, on six datasets, at six levels of granularity. We analyze the effects of these variables on summary quality as rated by participants and completion time of the experiment tasks. Our analysis shows that Sequence Synopsis produces the highest‐quality visual summaries for all three tasks, but understanding Sequence Synopsis results also takes the longest time. We also find that the participants evaluate visual summary quality based on two aspects: content and interpretability. We discuss the implications of our findings on developing and evaluating new visual summarization techniques.

Influence is a complex and subtle force that changes the behavior of involved users. Measuring influence can benefit to identify the influential users, and also benefit to provide important insights into the design of social platforms and applications. However, most existing work on social influence analysis has focused on homogeneous information networks. Few studies systematically investigate how to mine the strength of influence between nodes in heterogeneous information networks. In this paper, we present a meta path-based information entropy for modeling social influence in heterogeneous information networks (MPIE). Through setting meta paths, MPIE not only flexibly integrates heterogeneous information, but also obtains potential link information to measure the influence of nodes. Experiments on real data sets demonstrate the effectiveness of our proposed method.

With the increasing popularity of location technologies and location-based service applications, a large number of user's trajectory data have been collected. Publishing the real-time statistics data of trajectory streams can be useful in many fields such as intelligent transportation system, urban road planning and road congestion detection. As the trajectory data itself contains a wealth of user's privacy information, the privacy leakage problem has aggravated the risk of data publishing. In order to realize the personalized and uniform privacy preserving of user's trajectory data, the differential privacy model based on data perturbation is introduced, and a privacy preserving algorithm is proposed. The algorithm contains three modules of dynamic privacy budget allocation, privacy approximation and privacy publishing. In the experiment, the performances of the proposed method are verified by using real-life datasets.