Graph-structured data describing entities and their properties has become a notable component of the Web. With the increasing size of data graphs, an entity is often associated with too many property values to be entirely shown to the user, thereby requiring a compact but characteristic summary to present its most distinguishing features. This paper aims to automatically generate such characteristic entity summaries for human users. To achieve it, we exploit the informativeness of property values by analyzing the data graph using information theory. To improve the utility of information carried by a summary, we learn it from a text corpus. To reduce the information redundancy of a summary, we perform logical reasoning and measure similarity with statistical support. We formalize the entity summarization problem considering these factors as combinatorial optimization problems to solve. Experiments based on a real data graph and hand-crafted gold standards show that our approach improves on two state-of-the-art approaches in F-measure by 20.63%-38.79%.

Whether it be congestion control in cities or massive access in the Internet, these dynamic behaviors can be abstracted as flow demand between origin-destination node pairs (OD pairs) in the network. Links in complex systems are with notable heterogeneity, which means the volume of flow varies greatly, resulting in some critical links being more likely to congest under the flow attack, reducing the service capability of the system, and even causing network collapses. To explore how flow dynamics influences the network functionality and stability under congestion, we propose a link-based flow attack strategy that significantly degrades the service capability between OD pairs. In this approach, we first extract the routing paths and score the vulnerability of links between OD pairs, then an attack flow allocation rule based on critical links is designed to efficiently attack the target flow between OD pairs. Experiments on real-world networks show that the proposed strategy can quickly identify critical links and accurately attack the target flow. Besides, the proposed method provides positive and unique insights for defense strategy and network topology optimization.

In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions, called entity summaries. Existing solutions to entity summarization are mainly unsupervised. In this paper, we present a supervised approach NEST that is based on our novel neural model to jointly encode graph structure and text in KGs and generate high-quality diversified summaries. Since it is costly to obtain manually labeled summaries for training, our supervision is weak as we train with programmatically labeled data which may contain noise but is free of manual work. Evaluation results show that our approach significantly outperforms the state of the art on two public benchmarks.

Triple-structured open data creates value in many ways. However, the reuse of datasets is still challenging. Users feel difficult to assess the usefulness of a large dataset containing thousands or millions of triples. To satisfy the needs, existing abstractive methods produce a concise high-level abstraction of data. Complementary to that, we adopt the extractive strategy and aim to select the optimum small subset of data from a dataset as a snippet to compactly illustrate the content of the dataset. This has been formulated as a combinatorial optimization problem in our previous work. In this article, we design a new algorithm for the problem, which is an order of magnitude faster than the previous one but has the same approximation ratio. We also develop an anytime algorithm that can generate empirically better solutions using additional time. To suit datasets that are partially accessible via online query services (e.g., SPARQL endpoints for RDF data), we adapt our algorithms to trade off quality of snippet for feasibility and efficiency in the Web environment. We carry out extensive experiments based on real RDF datasets and SPARQL endpoints for evaluating quality and running time. The results demonstrate the effectiveness and practicality of our proposed algorithms.