Information diffusion prediction aims to forecast the temporal spread of opinions and behaviors by identifying potential adopters. Existing methods typically treat information diffusion as a sequence of individual adoptions and rely on computationally expensive pairwise (one-to-one) influence computations, often restricting predictions to just the next adopter. This individual-level paradigm both misrepresents real-world collective (many-to-many) influences and suffers a critical efficiency trade-off: to remain feasible, such models must truncate long diffusion histories, thereby overlooking early initiators and opinion leaders. To overcome these limitations, we formalize a more practical task: Group-based Information Diffusion Prediction, and propose an effective and scalable GRID framework. Specifically, GRID first learns group-oriented graph embeddings via a task-regularized information bottleneck objective, which amplifies key influence pathways and produces reliable user embeddings for group identification. Built on these embeddings, the core GroupAttn module captures inter-group influence while reducing complexity from quadratic to linear in cascade length. This enables the modeling of ultra-long cascades (exceeding 10,000 users) without truncation while preserving representational fidelity within a provable error bound. Finally, a group-wise objective guides the model to predict semantically meaningful future groups. Extensive experiments on four real-world datasets show that GRID outperforms ten state-of-the-art baselines by an average of 10.65% in accuracy, while achieving an order-of-magnitude gain in efficiency and extending the supported cascade length by up to 10 times.

Information diffusion prediction is fundamental for forecasting user participation in information sharing on social networks, such as retweets on Twitter. Existing methods typically extract user relationships from social networks and historical interactions, while further capturing contextual information within the specific diffusion process. However, these methods have several limitations: (1) They often utilize sequential diffusion process for prediction and simplify differentiated influences among participants; (2) They capture user relationships on the entire graph for all users, in which most information is not necessary for a specific diffusion process and is too inefficient for real-world large-scale networks. To tackle these limitations, we propose a novel and scalable model SILN, for sphere-based information diffusion prediction on large social networks. Specifically, SILN features three components. First, we integrate two kinds of sphere effects in terms of structural and temporal views, which learn an enhanced cascade representation. Second, SILN designs an efficient learning scheme based on the cascade-specific subgraph, which significantly reduces the entire graph computation to smaller subgraphs. Third, to facilitate subgraph extraction, we develop an optimized graph storage technique to allow constant-time neighbor access and reduce the storage cost by about 30% in practice. Extensive experiments on six real-world datasets validate that SILN consistently outperforms seven state-of-the-art competitors in prediction performance while exhibiting exceptional time and space efficiency on million-node social networks.

Existing knowledge graph query approaches, whether traditional textual query languages or visual query languages, have steep learning curves that are unfriendly for non-expert users. This demonstration presents a Visual Query approach based on Full-Text search for knowledge graphs, called VQFT, which simplifies the process of querying knowledge graphs for users. Inspired by full-text search techniques, VQFT aims to combine the user-friendliness of visual query with the intuitiveness of full-text search , enabling users to query knowledge graphs as straightforward as using a search engine. Faceted full-text indexes, visual query constructor , and an interactive user interface are designed to achieve this goal. User tests and surveys have demonstrated that VQFT is more user-friendly and easier to learn than existing methods, which simplifies the construction of knowledge graph queries for non-expert users.

Node attribute label inference is an important problem in attributed networks. Most existing works assume that node labels are at a single level, but in practice, the attribute labels can always be organized in a hierarchical structure according to their semantics. In this paper, we propose a novel hierarchical label inference model for attributed networks. Specifically, we propose a triple attention mechanism to extract fine-grained label semantics from three levels: hierarchical, sibling and global. Next, we propose the semantic fully-connected layer to explicitly exploit label semantics for attribute inference. We also propose semantic label propagation to enhance the interaction between the label semantics and the attributed network, and this interaction enables nodes in the attributed network to realise the proximity assumption at the label semantic level. Finally, we combine the semantic fully-connected layer with semantic label propagation for top-down hierarchical attribute inference. Extensive experiments demonstrate the superiority of our model.

Reachability query is a fundamental problem and has been well studied on static graphs. However, in the real world, the graphs are not static but always evolving over time. In this paper, we study the problem of historical reachability query on evolving graphs. We propose a novel index, named HR-Index, which integrates complete and correct historical reachability information of the evolving graph. A historical reachability query on an evolving graph can be converted into a static reachability query on its HR-Index and thus query efficiency can be improved significantly. We also propose two optimization techniques to reduce the size of HR-Index effectively. We confirm the effectiveness and efficiency of our method through conducting extensive experiments on real-life datasets. Experimental results show both vertex and edge size of HR-Index are far smaller than that of the evolving graphs and our method has at least an order of magnitude improvement in time and space efficiency compared to the state-of-the-art method.

In the social network, each user has attributes for self-description called user attributes, which are semantically hierarchical. Attribute inference has become an essential way for social platforms to realize user classifications and targeted recommendations. Most existing approaches mainly focus on the flat inference problem neglecting the semantic hierarchy of user attributes, which will cause serious inconsistency in multi-level tasks. In this article, we propose a multi-level model MLI, where information propagation part collects attribute information by mining the global graph structure, and the attribute correction part realizes the mutual correction between different levels of attributes. Further, we put forward the concept of generalized semantic tree, a way of representing the hierarchical structure of user attributes, whose nodes are allowed to have multiple parent nodes unlike the regular tree. Both regular and generalized semantic trees are commonly used in practice, and can be handled by our model. Besides, by making the inference start from sub-networks with sufficient attribute information, we design a “Ripple” algorithm to improve the efficiency and effectiveness of our model. For evaluation purposes, we conduct extensive verification experiments on DBLP datasets. The experimental results show the superior effect of MLI, compared with the state-of-the-art methods.

Evaluations of indoor earthquake safety actions can provide effective guidance for occupants, and can be beneficial for reducing earthquake-induced casualties. Accordingly, in this study, a virtual reality (VR)-based evaluation method is proposed for indoor earthquake safety actions. Specifically, an indoor seismic damage scene is constructed based on the Federal Emergency Management Agency (FEMA) P-58 method and a physics engine, by which the damage and movements of indoor nonstructural components can be accurately calculated. Subsequently, a virtual body model is created, and a corresponding inverse kinematics (IK) algorithm is developed to accurately track the earthquake safety actions of occupants. Finally, a human safety model based on collision kinetic energy and fatal conditions is designed for quantitatively evaluating the effectiveness of earthquake safety actions. Taking an office room and living room as case studies, three earthquake safety actions (i.e., “drop, cover, and hold on,” “triangle of life,” and “run outside”) are evaluated using the proposed method. The results indicate that “drop, cover, and hold on” is the safest action in most earthquake scenarios. The outcomes of this study can be used to rate the effectiveness of various earthquake safety actions, and to support occupant safety decision-making.

With the popularity of knowledge graphs growing rapidly, large amounts of RDF graphs have been released, which raises the need for addressing the challenge of distributed subgraph matching queries. In this paper, we propose an efficient distributed method to answer subgraph matching queries on big RDF graphs using MapReduce. In our method, query graphs are decomposed into a set of stars that utilize the semantic and structural information embedded RDF graphs as heuristics. Two optimization techniques are proposed to further improve the efficiency of our algorithms. One algorithm, called RDF property filtering , filters out invalid input data to reduce intermediate results; the other is to improve the query performance by postponing the Cartesian product operations. The extensive experiments on both synthetic and real-world datasets show that our method outperforms the close competitors S2X and SHARD by an order of magnitude on average.

Shortest path query is an important problem and has been well studied in static graphs. However, in practice, the costs of edges in graphs always change over time. We call such graphs as time-dependent graphs. In this paper, we study how to find a cost-optimal path with time constraint in time-dependent graphs. Most existing works regarding the Time-Dependent Shortest Path (TDSP) problem focus on finding a shortest path with the minimum travel time. All these works are based on the following fact: the earliest arrival time at a vertex v can be derived from the earliest arrival time at v 's neighbors. Unfortunately, this fact does not hold for our problem. In this paper, we propose a novel algorithm to compute a cost-optimal path with time constraint in time-dependent graphs. We show that the time and space complexities of our algorithm are O ( kn log n + mk ) and O (( n + m ) k ) respectively. We confirm the effectiveness and efficiency of our algorithm through conducting experiments on real datasets with synthetic cost.

Shortest path query is an important problem in graphs and has been well-studied. However, most approaches for shortest path query are based on single-cost (weight) graphs. In this paper, we introduce the definition of multi-cost graph and study a novel query: the optimal path query over multi-cost graphs. We propose a best-first branch and bound search algorithm with two optimizing strategies. Furthermore, we propose a novel index named k-cluster index to make our method more space and time efficient for large graphs. We discuss how to construct and utilize k-cluster index. We confirm the effectiveness and efficiency of our algorithms using real-life datasets in experiments.