The widespread use of social-media graphs has provided a convenient channel for rumor propagation. Rapid localization of rumor sources is therefore crucial for mitigating diffusion and enabling punitive countermeasures. Source Localization (SL) aims to identify the origin nodes given partial infection observations. Although deep-learning-based SL approaches outperform traditional estimators, three fundamental limitations remain: (i) Model Complexity —existing methods enrich node embeddings with cascades of auxiliary features, yielding high-capacity but excessively complex representations, leading to an exponential increase in the number of model parameters; (ii) Annotation gap —to overcome the scarcity of real-world misinformation cascades, current pipelines repeatedly simulate diffusion from a fixed seed, eroding robustness on true, few-shot outbreaks; and (iii) Computational bottleneck —full-model retraining or recurrent cascade simulation is required for every new task, which disqualifies the solutions from real-time deployment. Inspired by the success of prompt learning in NLP and graph learning, we propose LAPS, a Lightweight privilege-Allocation Prompting framework for Source localization. LAPS first trims parameter explosion and data scarcity by pre-training a graph-level source region classifier on adaptive subgraphs with source-prior diffusion data. It then enables few-shot SL via a privilege-allocation prompt module that updates <1% of all the parameters, avoiding model retraining to facilitate efficiency. Extensive experiments on five real-world networks demonstrate the effectiveness and efficiency of our prompt-based framework on few-shot source localization task.

Temporal heterogeneous network representation learning is a pivotal approach for encapsulating the diversity of nodes and edges along with their temporal evolution into concise, low‐dimensional node representations. This technique has demonstrated remarkable efficacy in various network analysis and inference tasks. However, existing approaches study network evolution mainly by analyzing snapshots of temporal networks, while neglecting the intrinsic formation mechanisms of temporal heterogeneous networks. Few dynamic models delve into the intrinsic factors propelling network evolution. To fill this research gap, we introduce a novel learning framework for temporal heterogeneous network representation learning with dynamic influence modeling, denoted as THNRD. THNRD pioneers the application of the Hawkes process to temporal heterogeneous networks, utilizing the linking process of dynamic events to emulate the network’s formation mechanism, capturing the intrinsic dynamic progression of temporal heterogeneous networks. Subsequently, THNRD introduces a multilayer spatiotemporal aggregation model under a unified spatiotemporal framework, which is designed to harmoniously integrate the semantic and dynamic attributes of the networks. We also take node influence into consideration to further describe the temporal emergent phenomena. We verify the effectiveness of our proposed method via extensive experimental evaluations on real‐world datasets. The results consistently demonstrate that THNRD outperforms current state‐of‐the‐art methods.

Dynamic graph representation learning has emerged as a pivotal paradigm for modeling time-varying relational patterns in complex systems ranging from social networks to urban mobility. While existing methods achieve notable progress in temporal modeling, one critical challenge remains insufficiently addressed: identifying dual temporal evolution, i,e., instantaneous states and evolutionary trajectories. To address the challenge, we propose HMGNN, a novel dynamic graph learning framework that harmoniously integrates temporal dynamics modeling with stable structural representation learning, allowing adaptive pattern discovery while preserving feature consistency in evolving environments. Firstly, we propose a dynamic model that integrates a historical information perception module and a temporal aggregation module. The module converts the historical information into the model and adaptively measures the impact of the instantaneous and historical information effectively through the aggregation function. Secondly, we devise a dual-component model learning framework comprising contrastive learning and multi-granular temporal curriculum learning to holistically capture evolutionary dynamics. The contrastive learning component employs continuous-view contrastive alignment to preserve stable node feature across temporal evolution. Complementarily, our multi-granular temporal curriculum learning introduces masking mechanism to explicitly learn different time interval evolution patterns. Extensive experiments demonstrate the significant superiority of HMGNN against state-of-the-art dynamic graph learning methods in terms of all evaluation metrics.

Information diffusion prediction, the forecasting of propagation paths, provides critical insights into information spread mechanisms, directly enabling applications like misinformation spread forecasting and detection for malicious account. Prior research primarily focused on combining user social graphs and information cascades for prediction, often overlooking the distinct role characteristics users exhibit during interactions. Classifying users into different roles enables the construction of a multi-layered social graph, facilitating the extraction of deeper user features. This paper introduces a model that leverages multi-dimensional interactions between user features. Specifically, to account for users' dynamic preferences, we construct sequential hypergraphs from information cascades using timestamps and utilize a hypergraph neural network to extract users' dynamic features. Furthermore, to capture users' static features, we build multi-layer social networks from the social graph based on users' roles. We employ graph convolutional networks to separately extract static features from each layer and subsequently fuse them using an attention mechanism. Superior performance of our framework is evidenced by experimental validation on real-world datasets against cutting-edge benchmarks.

In the contemporary era, characterized by an accelerated development in the domain of social networks, the phenomenon of fake news has attained unprecedented levels of prevalence, exerting substantial detrimental influence on society. Identification of the sources of such information in a timely manner is of paramount importance in order to prevent further damage. Existing source localization methods can be categorized into two distinct approaches: the first involves the deployment of observers followed by localization, while the second employs traditional community partitioning for source localization without considering community structure in observer deployment, resulting in suboptimal information acquisition. To address this issue, we propose Community Partition-Based Source Localization with Adaptive Observers Deployment (CSOL), which consists of three stages: In the first stage, community partitioning is achieved using contrastive learning with optimization and a feature extraction module that is highly correlated with partition. In the second stage, we are the first work to adaptively deploy observer based on community importance, integrating community partitioning with observer placement. In the third stage, an early source estimation strategy is employed to enhance efficiency and accuracy. Experimental results in real-world networks demonstrate that CSOL outperforms other SOTA methods in both accuracy and efficiency.

Heterogeneous information networks (HINs) are a key resource in many domain-specific retrieval and recommendation scenarios and in conversational environments. Current approaches to mining graph data often rely on abundant supervised information. However, supervised signals for graph learning tend to be scarce for a new task and only a handful of labeled nodes may be available. Meta-learning mechanisms are able to harness prior knowledge that can be adapted to new tasks. In this article, we design meta-learning framework for heterogeneous information networks ( META-HIN ), for few-shot learning problems on HINs. To the best of our knowledge, we are among the first to design a unified framework to realize the few-shot learning of HINs and facilitate different downstream tasks across different domains of graphs. Unlike most previous models, which focus on a single task on a single graph, META-HIN is able to deal with different tasks (node classification, link prediction, and anomaly detection are used as examples) across multiple graphs. Subgraphs are sampled to build the support and query set. Before being processed by the meta-learning module, subgraphs are modeled via a structure module to capture structural features. Then, a heterogeneous Graph Neural Network module is used as the base model to express the features of subgraphs. We also design a Generative Adversarial Network-based contrastive learning module that is able to exploit unsupervised information of the subgraphs. In our experiments, we fuse several datasets from multiple domains to verify META-HIN ’s broad applicability in a multiple-graph scenario. META-HIN consistently and significantly outperforms state-of-the-art alternatives on every task and across all datasets that we consider.

In network representation learning we learn how to represent heterogeneous information networks in a low-dimensional space so as to facilitate effective search, classification, and prediction solutions. Previous network representation learning methods typically require sufficient task-specific labeled data to address domain-specific problems. The trained model usually cannot be transferred to out-of-domain datasets. We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network. Unlike traditional network representation learning models that have to train the entire model all over again for every downstream task and dataset, PF-HIN only needs to fine-tune the model and a small number of extra task-specific parameters, thus improving model efficiency and effectiveness. During pre-training, we first transform the neighborhood of a given node into a sequence. PF-HIN is pre-trained based on two self-supervised tasks, masked node modeling and adjacent node prediction. We adopt deep bi-directional transformer encoders to train the model, and leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In the fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification, and node clustering. PF-HIN outperforms state-of-the-art alternatives on each of these tasks, on four datasets.

The existence of anomalies is quite common, but they are hidden within the complex structure and high-dimensional node attributes of the attributed networks. As a latent hazard in existing systems, anomalies can be transformed into important instruction information once we detect them, e.g., computer network admins can react to the leakage of sensitive data if network traffic anomalies are identified. Extensive research in anomaly detection on attributed networks has proposed various techniques, which do improve the quality of data in networks, while they rarely cope with the few-shot anomaly detection problem. Few-shot anomaly detection task with only a few dozen labeled anomalies is more practical since anomalies are rare in number for real-world systems.

Content representation is a fundamental task in information retrieval. Representation learning is aimed at capturing features of an information object in a low-dimensional space. Most research on representation learning for heterogeneous information networks (HINs) focuses on static HINs. In practice, however, networks are dynamic and subject to constant change. In this article, we propose a novel and scalable representation learning model, M-DHIN , to explore the evolution of a dynamic HIN. We regard a dynamic HIN as a series of snapshots with different time stamps. We first use a static embedding method to learn the initial embeddings of a dynamic HIN at the first time stamp. We describe the features of the initial HIN via metagraphs, which retains more structural and semantic information than traditional path-oriented static models. We also adopt a complex embedding scheme to better distinguish between symmetric and asymmetric metagraphs. Unlike traditional models that process an entire network at each time stamp, we build a so-called change dataset that only includes nodes involved in a triadic closure or opening process, as well as newly added or deleted nodes. Then, we utilize the above metagraph-based mechanism to train on the change dataset. As a result of this setup, M-DHIN is scalable to large dynamic HINs since it only needs to model the entire HIN once while only the changed parts need to be processed over time. Existing dynamic embedding models only express the existing snapshots and cannot predict the future network structure. To equip M-DHIN with this ability, we introduce an LSTM-based deep autoencoder model that processes the evolution of the graph via an LSTM encoder and outputs the predicted graph. Finally, we evaluate the proposed model, M-DHIN , on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.

To represent a complex network, paths are often employed for capturing relationships among node: random walks for (homogeneous) networks and metapaths for heterogeneous information networks (HINs). However, there is structural (and possibly semantic) information loss when using paths to represent the subgraph between two nodes, since a path is a linear structure and a subgraph often is not. Can we find a better alternative for network embeddings? We offer a novel mechanism to capture the features of HIN nodes via metagraphs, which retains more structural and semantic information than path-oriented models. Inspired by developments in knowledge graph embedding, we propose to construct HIN triplets using nodes and metagraphs between them. Metagraphs are generated by harnessing the GRAMI algorithm, which enumerates frequent subgraph patterns in a HIN. Subsequently, the Hadamard function is applied to encode relationships between nodes and metagraphs, and the probability whether a HIN triplet can be evaluated. Further, to better distinguish between symmetric and asymmetric cases of metagraphs, we introduce a complex embedding scheme that is able to precisely express fine-grained features of HIN nodes. We evaluate the proposed model, M-HIN, on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.