Yuanyuan Zhu

Wuhan University | WHU ·  College of Computer Science

Graph matching plays an essential role in many real applications. In this paper, we study how to match two large graphs by maximizing the number of matched edges, which is known as maximum common subgraph matching and is NP-hard. To find exact matching, it cannot handle a graph with more than 30 nodes. To find an approximate matching, the quality c...

Querying similar graphs in graph databases has been widely studied in graph query processing in recent years. Existing works mainly focus on subgraph similarity search and supergraph similarity search. In this paper, we study the problem of finding top-k graphs in a graph database that are most similar to a query graph. This problem has many applic...

A graph models complex structural relationships among objects, and has been prevalently used in a wide range of applications. Building an automated graph classification model becomes very important for predicting unknown graphs or understanding complex structures between different classes. The graph classification framework being widely used consis...

Graph matching plays an essential role in many real applications. In this paper, we study how to match two large graphs by maximizing the number of matched edges, which is known as maximum common subgraph matching and is NP-hard. To find exact matching, it cannot handle a graph with more than 30 nodes. To find an approxi-mate matching, the quality...

Querying cohesive subgraphs on temporal graphs (e.g., social network, finance network, etc.) with various conditions has attracted intensive research interests recently. In this paper, we study a novel Temporal $(k,\mathcal {X})$ -Core Query (TXCQ) that extends a fundamental Temporal $k$ -Core Query (TCQ) proposed in our conference paper by opt...

Querying cohesive subgraphs on temporal graphs (e.g., social network, finance network, etc.) with various conditions has attracted intensive research interests recently. In this paper, we study a novel Temporal $(k,\mathcal{X})$-Core Query (TXCQ) that extends a fundamental Temporal $k$-Core Query (TCQ) proposed in our conference paper by optimizing...

The Point-of-Interest (POI) transition behaviors could hold absolute sparsity and relative sparsity very differently for different cities. Hence, it is intuitive to transfer knowledge across cities to alleviate those data sparsity and imbalance problems for next POI recommendation. Recently, pre-training over a large-scale dataset has achieved grea...

While promoting a business or activity in geo-social networks, the geographical distance between its location and users is critical. Therefore, the problem of Distance-Aware Influence Maximization (DAIM) has been investigated recently. The efficiency of DAIM heavily relies on the sample location selection. Specifically, the online seeding performan...

Given a network with social and spatial information, cohesive group queries aim to find a group of strongly connected and closely co-located users. Most existing studies limit to finding groups with either the strongest social ties under certain spatial constraints or the minimum spatial distance under certain social constraints. It is difficult fo...

In modern smart cities, an increasing number of businesses rely on social network marketing to capture potential customers and third-party delivery systems to serve them; this system is called “online to offline”. Consequently, the well-known business location planning problem, which is used to attract nearby offline users, must be redefined. Thus,...

Location has a great impact on the success of many businesses. The existing works typically utilize the number of customers who are the Reverse Nearest Neighbors (RNN) of a business location to assess its goodness. While, with the prevalence of word-of-mouth marketing in social networks, a business can now exploit the social influence to attract en...

Influence maximization, aiming to select k seed users to influence the rest of users maximally, is a fundamental problem in social networks. Due to its well-known NP-hardness, great efforts have been devoted to developing scalable algorithms in the literature. However, the scalability issue is still not well solved in the time-sensitive influence m...

Keyword search problem has been widely studied to retrieve relevant substructures from graphs. However, existing approaches aim at finding compact trees/subgraphs containing the keywords, and ignore density to evaluate how strongly and stablely the keyword nodes are connected. In this paper, we study the problem of finding cohesive subgraph contain...

Query result diversification is critical for improving users’ query satisfaction by making the top ranked results cover more different query semantics. The state-of-the-art works address the problem via bi-criteria (namely, relevance and dissimilarity) optimization. However, such works only consider how dissimilar the returned results are to each o...

Searching similar graphs in graph databases for a query graph has attracted extensive attention recently. Existing works on graph similarity queries are threshold based approaches which return graphs with distances to the query smaller than a given threshold. However, in many applications the number of answer graphs for the same threshold can vary...

Background: Aligning protein-protein interaction (PPI) networks is very important to discover the functionally conserved sub-structures between different species. In recent years, the global PPI network alignment problem has been extensively studied aiming at finding the one-to-one alignment with the maximum matching score. However, finding large...

In recent years, a few researches focus on the similarity measure of semantic trajectories in road networks, since semantic trajectories in road networks have smaller volumes, higher qualities and can better reflect user behaviors. However, these works do not further discuss how to efficiently search similar trajectories. Thus, to implement an effi...

Query result diversification has drawn great research interests in recent years. Most previous work focuses on finding a locally diverse subset of a given finite result set, in which the results are as dissimilar to each other as possible. However, such a setup may not always hold. Firstly, we may need the result set to be globally diverse with res...

Exploring a knowledge graph through keyword queries to discover meaningful patterns has been studied in many scenarios recently. From the perspective of query understanding, it aims to find a number of specific interpretations for ambiguous keyword queries. With the assistance of interpretation, the users can actively reduce the search space and ge...

This paper addresses the classical triangle listing problem, which aims at enumerating all the tuples of three vertices connected with each other by edges. This problem has been intensively studied in internal and external memory, but it is still an urgent challenge in distributed environment where multiple machines across the network can be utiliz...

Graph is a powerful tool to model interactions in disparate applications, and how to assess the structure of a graph is an essential task across all the domains. As a classic measure to characterize the connectivity of graphs, clustering coefficient and its variants are of particular interest in graph structural analysis. However, the largest of to...

This paper addresses the classical triangle listing problem, which aims at enumerating all the tuples of three vertices connected with each other by edges. This problem has been intensively studied in internal and external memory, but it is still an urgent challenge in distributed environment where multiple machines across the network can be utiliz...

With the development of the positioning technology, studies on trajectories have been growing rapidly in the past decades. As a fundamental part involved in trajectory recommendation and prediction, trajectory similarity has attracted considerable attention from researchers. However, most existing works focus on raw trajectory similarity by compari...

Graphs have been widely used due to its expressive power to model complicated relationships. However, given a graph database DG = {g1, g2, · · · , gn}, it is challenging to process graph queries since a basic graph query usually involves costly graph operations such as maximum common subgraph and graph edit distance computation , which are NP-hard....

Graphs have been widely used due to its expressive power to model complicated relationships. However, given a graph database DG = {g1, g2, · · · , gn}, it is challenging to process graph queries since a basic graph query usually involves costly graph operations such as maximum common subgraph and graph edit distance compu-tation, which are NP-hard....

Social and information networks have been extensively studied over years. In this paper, we concentrate ourselves on a large information network that is composed of entities and relationships, where entities are associated with sets of keyword terms (kterms) to specify what they are, and relationships describe the link structure among entities whic...