The researchers focused on Cyprus, a country that maintained detailed surveillance data across the first four waves of the COVID-19 pandemic. Each confirmed case in their dataset carried a set of epidemiological attributes, including age, gender, date of infection, residency, and a classification of the case according to NACE, the European statistical classification of economic activities that encodes occupational context. Cases that had been linked by contact tracing formed the observed edges of an infection network, while the unlinked cases represented the missing structure the team hoped to recover. Framing the problem this way turns source attribution into a link prediction task: given two cases, can a model estimate how likely it is that one infected the other, even when no tracer ever documented that connection?

The first of the two approaches evaluated in the study relied on classical machine learning classifiers trained on engineered edge-level features. For every candidate pair of cases, the researchers combined the node-level attributes of the two individuals using a variety of operators, producing a feature vector that described the pair rather than either case alone. This design allowed the models to learn patterns such as whether infections were more likely between people of similar ages, overlapping infection dates, the same region of residence, or compatible occupational categories. Because the raw feature space constructed from all these combinations was large, the team applied Minimum Redundancy Maximum Relevance, or mRMR, to select features that were strongly associated with true transmission links while avoiding redundant information, and then refined the selection with greedy forward feature selection. Permutation importance was subsequently used to quantify how much each retained feature contributed to the classifiers’ decisions.

Among the classifiers tested, Random Forest and Gradient Boosting consistently delivered the strongest performance. During the first two pandemic waves, these models achieved F1-scores ranging from approximately 0.82 to 0.90, indicating a strong balance between precision and recall in distinguishing plausible transmission pairs from implausible ones. Mean Reciprocal Rank values, which measure how highly the true infector appears in a model’s ranked list of candidate sources, ranged from 0.55 to 0.64 for those early waves. In practical terms, this means that for a substantial share of unlinked cases, the correct or nearly correct infector sat at or near the top of the model’s shortlist, exactly the kind of ranking an epidemiologist would need to prioritize follow-up investigations.

Performance declined in the later waves, and the decline is itself informative. For the third and fourth waves, F1-scores ranged from 0.68 to 0.75 and MRR values dropped to between 0.23 and 0.33. The authors’ results suggest that the structure of transmission changed as the pandemic evolved: later waves in Cyprus were shaped by widespread community transmission, variants of concern, and vaccination, all of which blur the simple epidemiological signatures, such as tight date proximity or shared workplace, that made early-wave links easier to predict. When many candidate infectors share similar attributes with a case, the ranking task becomes fundamentally harder, and the numbers reflect that added ambiguity rather than any weakness in the method itself.

The second approach moved beyond hand-engineered features and turned to graph representation learning. The team employed a GraphSAGE model, a neural architecture that generates node embeddings by aggregating information from a node’s attributes and from the topology of the observed network around it. Each case is thus represented as a dense vector that encodes both what the case looks like epidemiologically and where it sits in the web of known connections. To score a candidate link between two cases, the researchers evaluated several strategies for combining the two embeddings, including concatenation, absolute difference, squared difference, the Hadamard product, and the dot product, each of which captures a different notion of similarity or compatibility between the pair.

Link prediction with graph representation learning achieved F1-scores ranging from roughly 0.70 to 0.79 and MRR values from 0.23 to 0.42 across all four pandemic waves. Compared with the classical classifiers, the GraphSAGE approach was somewhat less accurate in the early waves, where the engineered features captured the epidemiological signal very effectively, but it offered a more uniform level of performance across the entire pandemic period. This consistency matters for public health practice, because a tool that degrades sharply between epidemic phases is harder to integrate into routine surveillance workflows than one whose reliability is more stable. The embedding-based method also has the advantage of learning representations directly from the network, potentially capturing relational patterns that a fixed feature-engineering pipeline would miss.

The most consequential step of the study came after validation. Having confirmed that the best-performing classifier could reliably recover known links held out from the training data, the researchers applied it to the genuinely unlinked cases in the Cypriot dataset, asking the model to infer the most likely infector for each isolated node. The resulting reconstructed networks showed markedly fewer isolated components and larger connected structures than the original traced networks. Importantly, the reconstruction preserved epidemiologically relevant characteristics of the network, most notably the outdegree distribution, which reflects how many secondary infections are attributed to each case. Maintaining this property indicates that the inferred links did not distort the underlying transmission dynamics, such as the presence of superspreading individuals, but instead extended the observed network in a plausible direction.

The implications of this work extend well beyond one country or one pathogen. Incomplete infection networks are a chronic feature of outbreak response, from foodborne disease investigations to emerging zoonotic threats, and the quality of public health decisions depends heavily on how accurately those networks reflect reality. A reconstructed network with fewer isolated cases gives epidemiologists a more complete picture of transmission chains, enabling more targeted interventions: contact investigations can be prioritized toward the candidate sources ranked highest by the model, and containment resources can be directed at the connection points that matter most for interrupting spread. The authors note that such tools are particularly valuable during small epidemic waves, when case numbers are limited and every unexplained infection carries a proportionally larger informational cost.

The study also illustrates a broader lesson in applied machine learning for public health: classical feature-based models and modern graph neural networks are complementary rather than competing tools. Engineered epidemiological features, when chosen carefully with methods like mRMR and validated with permutation importance, can deliver excellent performance when the transmission signal is strong and interpretable. Graph representation learning, by contrast, offers robustness across changing epidemic conditions and the flexibility to incorporate network structure directly. As surveillance systems grow richer and more digitized, hybrid pipelines that combine both strategies, validated rigorously on known links before being trusted with unknown ones, are likely to become a standard component of the computational epidemiology toolkit, turning the frustrating gaps in contact tracing data from dead ends into testable hypotheses about the hidden architecture of disease spread.

Subject of Research: Link prediction in COVID-19 infection networks using machine learning and graph representation learning

Article Title: Predicting missing links in COVID-19 infection networks

Article References: Dimitriou, P. A., Silvestros, V., Constantinou, E., Pitris, C., & Kolios, P. (2026). Predicting missing links in COVID-19 infection networks. PLOS Complex Systems, 3(8), e0000124. https://doi.org/10.1371/journal.pcsy.0000124

Image Credits: AI Generated

DOI: 10.1371/journal.pcsy.0000124

Keywords: COVID-19, link prediction, machine learning, graph representation learning, GraphSAGE, contact tracing, epidemiology, Cyprus, Random Forest, Gradient Boosting, network reconstruction, public health

A team of researchers from the University of Alicante and the Universidad Politécnica de Cartagena in Spain has now proposed a way out of this trap. In an open-access paper published in Complex & Intelligent Systems, Carla Piñol, Manuel Curado, Jose F. Vicent, Leandro Tortosa and Antonio J. Banegas-Luna introduce return random walk kinship, or RRWK, a density-aware framework for extracting subgraphs that remains robust precisely where existing methods degrade. The work does not merely offer another heuristic; the authors also derive structural and computational bounds for their method, giving the field a principled account of how the extraction behaves as networks grow denser and larger. The result is a technique that, according to their experiments on both synthetic and real-world datasets, preserves the structural properties of the original graph more faithfully than state-of-the-art baselines while keeping computational costs competitive.

To appreciate why density is such a problem, it helps to look at how subgraph extraction is currently done. The most widespread strategy is the enclosing subgraph: given a target node, the algorithm collects its immediate neighbors, then the neighbors of those neighbors, and so on, up to some fixed number of hops. In a sparse network this works beautifully, because each expansion adds only a handful of new nodes. In a dense network, however, the first hop alone can sweep in an enormous crowd. A node with thousands of direct connections drags all of them into the subgraph, and by the second hop the extracted region may encompass most of the network. The computational burden explodes, and worse, the resulting structure is so diluted that the local organization of the graph, the fine-grained pattern of relationships that machine learning models need in order to reason about the target node, is effectively washed out.

A more recent line of work reformulated the problem as a local clustering procedure based on personalized PageRank, the famous algorithm behind web search that measures the importance of nodes by simulating random surfers who occasionally teleport back to a starting point. Applied to subgraph extraction, personalized PageRank assigns each node a probability of being reached by a walker that starts at the target and keeps stumbling through the network, and the highest-probability nodes are gathered into the subgraph. This approach produced better results than naive hop-based expansion, because it weighs nodes by their actual reachability rather than their raw distance. Yet the Spanish team identified a critical limitation: the quality of personalized PageRank extraction is itself sensitive to density. In highly connected networks, the walker’s probability mass spreads so widely that the extracted subgraph again loses cohesion, confining the method’s usefulness to networks of medium to low density.

RRWK attacks the problem from a different angle, built on a deceptively simple observation: what makes a node structurally kin to a target is not just that a random walk can reach it, but that the walk can come back. The framework is based on bounded outbound and return random-walk connectivity. Starting from a target node, the method sends out random walks of bounded length, but instead of scoring nodes solely by how often they are visited on the way out, it evaluates the connectivity of return paths, the alternative routes by which a walker can find its way back to the starting region. Nodes that participate in many return paths are, in a well-defined sense, woven into the same fabric as the target. They are not merely reachable; they are mutually reinforcing parts of a cohesive structure.

This return-path perspective is what gives RRWK its density resistance. In a dense network, the sheer number of alternative routes between two nodes is exactly what causes other methods to fail, because the neighborhood expansion becomes unmanageable. But for a return-walk-based measure, those alternative routes are signal rather than noise: they are the evidence that two nodes belong to the same structurally cohesive region. By exploiting alternative return paths, RRWK jointly captures local and global organizational properties of the graph. The local property is the tight clustering of immediate neighbors; the global property is the way those clusters are anchored in the wider topology through redundant connectivity. Conventional enclosing-subgraph and personalized PageRank strategies, the authors argue, cannot hold both of these at once as density rises, whereas RRWK is specifically designed to maintain structural robustness in that regime.

A distinguishing feature of the paper is that it does not stop at an empirical demonstration. The researchers carry out a formal study of the structural and computational bounds of the proposed method, characterizing how the size and fidelity of the extracted subgraphs scale with the parameters of the random walks and with the density of the host graph. This kind of analysis matters for practitioners, because subgraph extraction sits at the base of the machine learning pipeline: if the extraction step is unpredictable, everything built on top of it inherits that unpredictability. By establishing bounds, the authors provide guarantees about the tractability of RRWK, showing that the method maintains competitive computational performance even in dense graph scenarios where competing approaches become computationally intractable or structurally meaningless.

The experimental evaluation spans both synthetic and real-world datasets, a combination that lets the authors isolate the effect of density in controlled settings while also demonstrating practical relevance. On synthetic networks, where the ground-truth community structure is known by construction, the team could measure precisely how well each method’s extracted subgraphs preserved the structural properties of the original graph as density was dialed up. On real-world datasets, the same fidelity question was asked in messier, more realistic conditions. Across both regimes, the reported outcome is consistent: RRWK preserves the structural properties of the original graph more accurately than state-of-the-art subgraph extraction baselines, with the advantage growing precisely in the dense regimes where the baselines falter, while its runtime remains competitive.

The implications reach well beyond graph theory. Link prediction, the task of guessing which connections will appear next in an evolving network, underlies friend recommendations, drug target discovery and knowledge graph completion, and it typically relies on subgraph extraction around candidate node pairs. If the extraction collapses in dense networks, the predictions built on it degrade silently. A method that stays structurally faithful under density could therefore sharpen a wide range of downstream applications, from analyzing densely connected biological interaction networks to studying financial systems where connectivity is high by design. The authors’ framing of subgraph extraction as a density-sensitive operation, rather than a solved preprocessing step, is itself a contribution that may redirect attention across the field.

The paper, which was received in April 2025, accepted in July 2026 and published in September 2026 under open access terms, was supported by Grant PID2025-175296OB-I00 funded by MICIU/AEI. Its authors span two Spanish institutions, with the core team at the Department of Computer Science and Artificial Intelligence in Alicante and a collaborator at the Centro Universitario de la Defensa in San Javier. As dense graphs become the norm rather than the exception, with ever-larger portions of science and commerce encoded as highly connected relational data, tools like RRWK address a bottleneck that will only tighten. By grounding a practical extraction algorithm in the mathematics of return random walks and backing it with explicit structural and computational bounds, the researchers have offered the graph machine learning community something rarer than a new benchmark win: a clearer understanding of when and why subgraph extraction works at all.

Subject of Research: Density-aware subgraph extraction from dense graphs using bounded outbound and return random-walk connectivity

Article Title: RRWK: structural and computational bounds for dense graph subgraph extraction

Article References: Piñol, C., Curado, M., Vicent, J. F., Tortosa, L., & Banegas-Luna, A. J. (2026). RRWK: structural and computational bounds for dense graph subgraph extraction. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-026-02453-7

Image Credits: AI Generated

DOI: 10.1007/s40747-026-02453-7

Keywords: subgraph extraction, dense graphs, random walks, graph representation learning, personalized PageRank, link prediction, structural preservation, complex networks, graph theory, machine learning, computational bounds, RRWK

The first bottleneck concerns the graphs themselves. Graph neural networks derive their expressive power from the quality of the graph structures they are given, but in the real world those structures are frequently noisy, incomplete, or in some cases entirely unavailable. A road network may contain erroneous connections recorded by faulty sensors, a communication network may be only partially observed, and many applications, such as certain molecular or social tasks, provide feature data without any explicit relational structure at all. If the graph fed into the model is flawed, the learned representations will be flawed as well, no matter how sophisticated the architecture may be. The second bottleneck is architectural: GNNs have historically been designed by hand, in a heuristic manner that requires specialist knowledge of both the domain and the model class, and manual design inevitably explores only a small fraction of the possible design space, leading to sub-optimal performance.

A team of researchers led by Yufeng Wang and Xinyi Wang of Nanjing University of Posts and Telecommunications, together with Jianhua Ma of Hosei University and Qun Jin of Waseda University, published a comprehensive survey on 23 September 2026 that addresses these twin challenges through a unified framework. Their work, titled Automated graph construction and graph neural network search, integrates two research threads that have largely evolved in parallel: graph structure learning, which attempts to construct or repair the graph itself, and graph neural architecture search, abbreviated GNAS, which automates the design of the network that processes it. By placing both inside a single pipeline for downstream tasks, the survey offers what the authors describe as a thoughtful insight into automated representation learning from the perspective of both the data, that is the graph structure, and the models, that is the automatically discovered GNN architectures.

The technical heart of the survey lies in its systematic categorization of graph structure learning paradigms. The authors divide the field into two broad families. Task-agnostic heuristic based strategies attempt to build or refine graph structures without direct reference to a specific downstream objective, often relying on similarity measures, k-nearest-neighbor constructions, or domain-specific rules that capture plausible relationships between nodes. These methods are flexible and reusable, but they carry no guarantee that the resulting structure is optimal for the prediction, classification, fault detection, or decision-making task the practitioner ultimately cares about. Task-aware, end-to-end learning based strategies, by contrast, treat the graph structure itself as a set of learnable parameters. The adjacency information is optimized jointly with the GNN weights against the loss function of the actual task, allowing the model to discover precisely those connections that improve predictive performance. The price of this adaptivity is higher computational cost and a greater risk of overfitting, especially when the number of candidate edges grows quadratically with the number of nodes.

The second pillar of the survey is a synthesis of graph neural architecture search, a discipline descended from the broader field of neural architecture search that transformed the design of convolutional and recurrent networks. The authors organize GNAS research around four technical dimensions. The first is the search space, which they split into micro and macro levels. The micro search space defines the possible individual GNN operations, such as graph convolution, graph attention, or message-passing variants, along with their aggregation functions and activation choices. The macro search space defines how these operations are wired together into complete architectures, ranging from fixed layer stacks to flexible multi-branch topologies. The choice of search space fundamentally determines which architectures can ever be discovered, making it the most consequential decision in any GNAS system.

The second dimension is architecture embedding, the mechanism by which candidate architectures are encoded so that a search algorithm can manipulate them. Some approaches encode architectures as discrete strings or graphs, while others employ continuous relaxations in which the discrete choice among operations is replaced by a weighted mixture, allowing gradient descent to optimize the mixture weights directly. This relaxation technique, popularized in earlier architecture search work, dramatically reduces search cost but introduces its own complications, including the risk that the continuously relaxed architecture performs poorly once the mixture is collapsed back to a single discrete model.

The third dimension covers search strategies, the algorithms that navigate the space of candidate architectures. The survey synthesizes approaches based on reinforcement learning, in which a controller is rewarded for architectures that perform well on a validation task; evolutionary algorithms, which maintain and evolve populations of candidate designs; differentiable methods that rely on gradient information; and Bayesian optimization or other surrogate-guided techniques. Each strategy occupies a different point on the trade-off curve between search cost and the quality of the final architecture, and the authors carefully analyze the applicability and limitations of each family within the graph domain, where evaluating a single candidate can be far more expensive than in conventional image classification settings.

The fourth dimension is performance evaluation methodology. Because a full training run for every candidate architecture would be prohibitively expensive, GNAS systems rely on shortcuts such as training for only a few epochs, training on reduced subsets of data, or learning a performance predictor that estimates a candidate’s value from its embedding. The fidelity of these estimators, and the degree to which a cheap proxy ranking matches the ranking a fully trained model would achieve, is a persistent source of error in architecture search, and the survey devotes attention to how graph-specific properties, such as varying graph sizes and sparse connectivity, complicate evaluation further.

Perhaps the most conceptually interesting contribution is the outline of interaction paradigms between graph structure learning and GNAS. Because the graph and the architecture are both learned rather than fixed, they can influence each other in different ways. In loosely coupled designs, the structure is learned first and then handed to the architecture search, or vice versa, while in tightly coupled designs the two processes are optimized jointly, so that the emerging architecture co-adapts to the emerging graph and the graph co-adapts to the architecture. The survey analyzes these interaction patterns and identifies open issues that remain unresolved, from the scalability of joint optimization to the theoretical understanding of when learned structures genuinely improve downstream generalization. The research was sponsored by the QingLan Project of Jiangsu Province and the Jiangsu Provincial Key Research and Development Program, and the article is published open access under a Creative Commons Attribution 4.0 license.

For practitioners, the significance of this unified view is hard to overstate. Automated machine learning has already reshaped how vision and language models are built, and this survey signals that graph-based AI is following the same trajectory. As demand grows for intelligent systems that understand physical infrastructure, from smart grids and traffic management to fault detection in communication networks, the ability to automatically construct the right graph and automatically discover the right network architecture could lower the barrier to deploying state-of-the-art graph intelligence in domains where human expertise is scarce. The survey does not claim to have solved these problems, but by mapping the landscape of graph structure learning and graph neural architecture search into a common pipeline, it gives researchers a coherent framework for the next generation of automated graph representation learning, where both the data and the model learn to build themselves.

Subject of Research: Automated graph construction and graph neural architecture search for graph representation learning

Article Title: Automated graph construction and graph neural network search: a survey

Article References: Wang, Y., Wang, X., Ma, J., & Jin, Q. (2026). Automated graph construction and graph neural network search: a survey. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11710-7

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11710-7

Keywords: graph neural networks, graph structure learning, neural architecture search, automated machine learning, deep learning, graph representation learning, search strategies, artificial intelligence, message passing, performance evaluation, survey, graph theory

The setting the researchers tackled, known as Online Continual Graph Learning, or OCGL, is among the harshest in machine learning. Nodes of a graph, which might represent Bitcoin transactions, scientific papers, social media posts or products on a shopping site, arrive one at a time in a continuous stream. Each new node brings its own attributes and connections to previously seen nodes, and the distribution of the data can shift at any moment, for instance when new classes of objects begin to appear. The model must make accurate predictions at every instant, adapt on the fly, and retain what it learned about earlier data, all under strict limits on memory and computation. Crucially, the model sees each node only once, so there is no possibility of the repeated offline training passes that standard deep learning relies on.

This streaming regime makes the phenomenon known as catastrophic forgetting especially damaging. When neural networks are updated sequentially on new data, the parameter changes that help with the new information often overwrite the representations that supported old tasks, erasing previously acquired knowledge. Graph data add a second, structural source of forgetting: because graph neural networks aggregate information from neighbors, the embedding of a node changes as new nodes attach to it, so even a perfectly stable classifier faces inputs that drift over time. Existing state-of-the-art methods typically fight forgetting with replay buffers that store examples of past data, or with regularization schemes that penalize changes to important weights, and both strategies carry substantial memory and computational costs in the graph setting.

The new approach, by contrast, decouples representation from prediction in a strikingly simple way. The authors split the model into two parts: a feature extractor that converts each node and its sampled neighborhood into a fixed-length embedding vector, and a lightweight classifier that maps embeddings to predictions. The feature extractor is a graph neural network whose weights are randomly initialized from an appropriate distribution and then frozen forever. Because the encoder never changes, the representations it produces cannot drift, and one of the two major sources of forgetting, the drift of backbone parameters, is eliminated by construction. Only the classifier is trained, and even that can be done without gradient descent using a streaming statistical method.

The researchers tested two flavors of randomized encoder. The first, called UGCN, is an untrained graph convolutional network with two layers of 1024 units each, whose layer outputs are concatenated into a 2048-dimensional node embedding that captures neighborhood information at multiple resolutions. The second, Graph Random Neural Features or GRNF, draws on theory showing that random samples from a universal family of graph neural networks can approximate kernel functions on graphs, and can provably separate any two non-isomorphic graphs. Both encoders operate on sparsified two-hop neighborhoods sampled around each node, keeping the computational footprint constant even as the graph grows denser. On top of either encoder, the team placed a Streaming Linear Discriminant Analysis classifier, which maintains running class means and a shared covariance matrix updated incrementally with each new example, requiring no memory buffer of past data at all.

The theoretical intuition behind the method is elegant. The authors formally decompose forgetting risk into three components: structural drift arising from the evolving graph itself, which no model can remove; backbone parameter drift, which the frozen encoder eliminates entirely; and classifier parameter drift, which for the streaming classifier shrinks as more examples of each class accumulate. Stability alone is not enough, of course, since a model must also remain plastic enough to absorb new classes and shifted distributions. That is where the over-parameterized random encoders earn their keep. Results from randomized network theory guarantee that, given a sufficiently large embedding dimension, randomly initialized features are expressive enough to make the downstream classification problem linearly separable, so a simple linear readout suffices.

The experimental results across seven benchmarks are remarkable. On six node-classification datasets, including the citation networks CoraFull and Arxiv, the co-purchase graph Amazon Computer, the Reddit post network, the heterophilic Roman Empire Wikipedia graph, and the Elliptic Bitcoin transaction network, the combination of randomized features and the streaming classifier generally beat every alternative considered, including experience replay, A-GEM, EWC, LwF and MAS applied to a conventionally trained graph network, as well as recent graph-specific replay methods such as PDGNN, SSM and TWP. In class-incremental streams, where new classes arrive in blocks, the approach approached the upper bound of joint offline training on the complete final graph, a ceiling that no online method can normally reach. Notably, the method needs no memory buffer, whereas replay-based competitors must store a substantial fraction of past nodes tailored to graph topology.

The robustness of the result is underlined by several control experiments. Even with as few as 64 random features, performance on most benchmarks matched or exceeded state-of-the-art trained methods, and gains had not saturated even at 4096 features. A frozen graph network pre-trained in a supervised way on part of the stream did not outperform the purely random encoder on most datasets, indicating that the advantage stems from the stability and richness of random representations rather than from any task-specific preparation. On the heterophilic Roman Empire dataset, where standard graph convolutions smooth neighboring features together, the more expressive GRNF encoder proved clearly superior. A hybrid extractor mixing features from both encoders delivered consistently robust performance across benchmarks. In memory terms, the fixed backbone plus streaming statistics occupied less space than the replay buffers of competing methods while sitting on the efficient frontier of the accuracy-versus-memory tradeoff.

The practical implications reach well beyond academic benchmarks. The Elliptic experiments, conducted on real Bitcoin transaction data with genuine timestamps in a time-incremental stream, demonstrate that the method works on realistic financial fraud detection problems where new transaction patterns emerge continuously. The same properties make the approach attractive for intrusion detection in Internet of Things networks, healthcare monitoring on temporal patient graphs, and recommendation systems, all applications the authors cite as motivation. Because the classifier updates are cheap streaming statistics and the encoder requires no training whatsoever, the method is naturally suited to deployment where latency is critical and predictions must be available at any moment. The authors caution that their conclusions are specific to the online setting, that a full theoretical analysis of forgetting remains future work, and that extensions to graph-level and edge-level tasks, regression and anomaly detection are still open. But the central message is already provocative: sometimes the most effective way for an artificial intelligence to remember is to stop changing its mind.

Subject of Research: Randomized fixed representations for mitigating catastrophic forgetting in online continual graph learning

Article Title: The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning

Article References: The Unreasonable Effectiveness of Randomized Representations in Online Continual Graph Learning. (n.d.). https://doi.org/10.1007/s10994-026-07128-5

Image Credits: AI Generated

DOI: 10.1007/s10994-026-07128-5

Keywords: continual learning, graph neural networks, catastrophic forgetting, online learning, randomized representations, streaming linear discriminant analysis, node classification, experience replay, graph representation learning, concept drift, machine learning, Bitcoin fraud detection