Developing a universal graph model capable of generalizing across diverse graph domains has consistently been a key objective in graph learning. Recently, many studies have focused on achieving in-context learning (ICL) on graphs, which can generalize to novel tasks without the need for fine-tuning, similar to large language models (LLMs) such as GPT-3. These researches can be primarily divided into graph-based methods and LLM-based methods. However, the generalization performance of the former is limited by the representation capability of GNNs, while the latter faces the challenge of LLMs understanding graph structures. Therefore, we propose CAGML, a context-aware graph meta-learning model, which learns to generalize to cross-domain and cross-granularity graph tasks using a meta-trained Transformer. Firstly, we formulate graph few-shot learning tasks as a structure-aware sequence modeling problem to unify cross-domain and cross-granularity tasks. Then, a structure-aware Transformer (SAT) is introduced as a graph in-context learner to make predictions with a few labels and the task-specific structural context. Finally, we pre-train SAT in a meta-optimization manner on large-scale citation network and knowledge graph. Experiments on 6 cross-domain graph datasets show that, without fine-tuning, CAGML can achieve state-of-the-art (SOTA) performance in terms of average performance across cross-granularity tasks on adopted datasets.

Website fingerprinting attacks leverage encrypted traffic features to identify specific services accessed by users within anonymity networks such as Tor. Although existing WF methods achieve high accuracy on static datasets using deep learning techniques, they struggle in dynamic environments where anonymous websites continually evolve. These methods typically require full retraining on composite datasets, resulting in substantial computational and storage burdens, and are particularly vulnerable to classification bias caused by data imbalance and concept drift. To address these challenges, we propose EIL-WF, a dynamic WF framework based on incremental learning that enables efficient adaptation to newly emerging websites without the need for full retraining. EIL-WF incrementally trains lightweight, independent classifiers for new website classes and integrates them through classifier normalization and energy alignment strategies grounded in energy-based model theory, thereby constructing a unified and robust classification model. Comprehensive experiments on two public Tor traffic datasets demonstrate that EIL-WF outperforms existing incremental learning methods by 6.2%–20.2% in identifying new websites and reduces catastrophic forgetting by 5.4%–20%. Notably, EIL-WF exhibits strong resilience against data imbalance and concept drift, maintaining stable classification performance across evolving distributions. Furthermore,EIL-WF decreases training time during model updates by 2–3 orders of magnitude, demonstrating substantial advantages over conventional full retraining paradigms.

Mining geographic location of social media users is a crucial technology for realizing the mapping of cyberspace to geographical world, which can provide strong support for wide-ranging location-based services. As a typical approach, user geolocation methods based on relationships rely on the assumption of location homophily between users and their neighbors. However, these methods only utilize the geographic influence between pair-wise relationship, resulting in undesired geolocation performance. In this paper, a social media user geolocation method based on geographically compact social subgraphs (SMUG-GCS) is proposed. Firstly, we analyze the relationship pattern among users in geographic proximity, and find a phenomenon that users who are geographically close tend to have tightly social groups. Based on this finding, a subgraph partitioning algorithm is presented which integrates structure compactness and geographical credibility to identify a set of subgraphs, whose nodes are more tightly connected and geographically proximity. Finally, user locations are inferred using the propagation of user information only based on the geographically compact subgraph. Extensive experiments are conducted on three real-world social media datasets. The results show that, compared with 5 typical relationship-based methods, SMUG-GCS improves the geolocating accuracy while reducing storage costs, leading to a significant reduction in median error distance ranging from 26.7% to 82.9%, as well as decrease in storage requirements by up to 56.5%.

User location discovery from social media is crucial for location-based services such as emergency awareness and event monitoring. Existing approaches generally integrate user-generated text features and social relationships but insufficiently explore location-specific features and geographically proximate relationships, leading to suboptimal accuracy. In this article, we propose a Twitter user geolocation method based on location feature enhancement to better capture the location characteristics in users’ tweets and social relationships. Specifically, a user tweet representation algorithm based on location feature separation (TwLS) is designed. By leveraging words’ location-aware weight matrix and pre-trained embeddings, TwLS calculates a tweet representation for each user in every location, explicitly indicating the relevance between users and various locations. Additionally, we develop the local celebrity discovery method (LocCel) to construct social networks by identifying and preserving geographically concentrated high-degree nodes while filtering noise. Thereby, LocCel enhances local relationships and strengthens location-proximate connections within the user social network. Experiments on two real-world datasets show that our method outperforms seven baselines, improving user geolocation accuracy by 3.1% ∼ 8.1% and 1.8% ∼ 8.8%, while reducing median error by 22.2% ∼ 52.8% and 19.4% ∼ 50.7%, respectively.

Social media user geolocation is to infer users’ resident locations based on social media data, including user texts and social relationships. Existing methods mainly rely on the textual feature propagation in the social graph to fuse users’ textual and social information. The geolocation accuracy is susceptible to insufficient data sources and inadequate fusion. In this paper, a social media user geolocation algorithm based on cyclic coupling (called UGCC) is proposed. We collapse the social graph based on the neighbor location proximity, which reduces noisy information while enriching social relationships. Unlike existing methods that ignore the social graph's structure, UGCC measures the probability of users being in the candidate locations according to users’ structural location in the social sub-graph. Finally, we design a cyclic coupling mechanism to fuse the users’ textual and social information, which enables the two kinds of information to enhance each other and geolocate users cooperatively. Compared with ten typical existing methods (such as RELP and HGNN), experimental results show UGCC's superior performance. On two public datasets, the city-level accuracies of UGCC reach 40.8% and 50.1%; the median errors are 35.1% and 23.4% lower than the state-of-the-art methods.

Information diffusion prediction aims to predict the tendency of information spreading in the network. Previous methods focus on extracting chronological features from diffusion paths and leverage relations in social graph as side information to facilitate diffusion prediction. However, abundant high-order social relations in information diffusion have not been sufficiently utilized, such as co-repose and co-following which can further mine potential user common preferences. In this paper, we construct a heterogeneous diffusion network (HDN) from the social graph and information cascades to model the high-order social relations in information diffusion. Then, we design a novel model named Multi-Channel Recurrent Graph Convolutional Network (MC-RGCN), which can extract high-order social relation semantics from the channels of HDN to promote prediction performance. In each channel, we depict a specific social relations from the views of global topology, pairwise strength, and local structure. Finally, we conduct extensive experiments on three real-world datasets, and the results show that our proposed method outperforms the state-of-the-art models on diffusion prediction.

Social network user location inference technology has been widely used in various geospatial applications like public health monitoring and local advertising recommendation. Due to insufficient consideration of relationships between users and location indicative words, most of existing inference methods estimate label propagation probabilities solely based on statistical features, resulting in large location inference error. In this paper, a Twitter user location inference method based on representation learning and label propagation is proposed. Firstly, the heterogeneous connection relation graph is constructed based on relationships between Twitter users and relationships between users and location indicative words, and relationships unrelated to geographic attributes are filtered. Then, vector representations of users are learnt from the connection relation graph. Finally, label propagation probabilities between adjacent users are calculated based on vector representations, and the locations of unknown users are predicted through iterative label propagation. Experiments on two representative Twitter datasets - GeoText and TwUs, show that the proposed method can accurately calculate label propagation probabilities based on vector representations and improve the accuracy of location inference. Compared with existing typical Twitter user location inference methods - GCN and MLP-TXT+NET, the median error distance of the proposed method is reduced by 18% and 16%, respectively.