arXiv subjects

Yijun Ran

Publications and source records attributed to Yijun Ran.

10 recordsLinked to original sources

arXiv2026

Inductive Dual-Polarity Modeling via Static-Dynamic Disentanglement for Dynamic Signed Networks

Dynamic signed networks (DSNs) are common in online platforms, where time-stamped positive and negative relations evolve over time. A core task in DSNs is dynamic edge prediction, which forecasts future relations by jointly modeling edge existence and polarity (positive, negative, or non-existent). However, existing dynamic signed network embedding (DSNE) methods often entangle positive and negative signals within a shared temporal state and rely on node-specific temporal trajectories, which can obscure polarity-asymmetric dynamics and harm inductive generalization, especially under cold-start evaluation. We study an inductive setting where each test edge contains at least one endpoint node held out from training, while its interactions prior to the prediction time are available as historical evidence. The model must therefore infer representations for unseen nodes solely from such limited history. We propose IDP-DSN, an Inductive Dual-Polarity framework for Dynamic Signed Networks. IDP-DSN maintains sign-selective memories to model positive and negative temporal dynamics separately, performs history-only neighborhood inference for unseen nodes (instead of learned node-wise trajectories), and enforces polarity-wise static--dynamic disentanglement via an orthogonality regularizer. Experiments on BitcoinAlpha, BitcoinOTC, Wiki-RfA, and Epinions demonstrate consistent improvements over the strongest baselines, achieving relative Macro-F1 gains of 16.8/23.4%, 16.9/24%, 30.1/25.5%, and 18.7/28.9% in the transductive/inductive settings, respectively. These results highlight the effectiveness of IDP-DSN on DSNs, particularly under inductive cold-start evaluation for dynamic signed edge prediction.

cs.SI↗

arXiv2025

A generalized motif-based Naïve Bayes model for sign prediction in complex networks

Signed networks, encoding both positive and negative interactions, are essential for modeling complex systems in social and financial domains. Sign prediction, which infers the sign of a target link, has wide-ranging practical applications. Traditional motif-based Naïve Bayes models assume that all neighboring nodes contribute equally to a target link's sign, overlooking the heterogeneous influence among neighbors and potentially limiting performance. To address this, we propose a generalizable sign prediction framework that explicitly models the heterogeneity. Specifically, we design two role functions to quantify the differentiated influence of neighboring nodes. We further extend this approach from a single motif to multiple motifs via two strategies. The generalized multiple motifs-based Naïve Bayes model linearly combines information from diverse motifs, while the Feature-driven Generalized Motif-based Naïve Bayes (FGMNB) model integrates high-dimensional motif features using machine learning. Extensive experiments on four real-world signed networks show that FGMNB consistently outperforms five state-of-the-art embedding-based baselines on three of these networks. Moreover, we observe that the most predictive motif structures differ across datasets, highlighting the importance of local structural patterns and offering valuable insights for motif-based feature engineering. Our framework provides an effective and theoretically grounded solution to sign prediction, with practical implications for enhancing trust and security in online platforms.

cs.CR↗

arXiv2025

Identifying social bots via heterogeneous motifs based on Naïve Bayes model

Identifying social bots has become a critical challenge due to their significant influence on social media ecosystems. Despite advancements in detection methods, most topology-based approaches insufficiently account for the heterogeneity of neighborhood preferences and lack a systematic theoretical foundation, relying instead on intuition and experience. Here, we propose a theoretical framework for detecting social bots utilizing heterogeneous motifs based on the Naïve Bayes model. Specifically, we refine homogeneous motifs into heterogeneous ones by incorporating node-label information, effectively capturing the heterogeneity of neighborhood preferences. Additionally, we systematically evaluate the contribution of different node pairs within heterogeneous motifs to the likelihood of a node being identified as a social bot. Furthermore, we mathematically quantify the maximum capability of each heterogeneous motif, enabling the estimation of its potential benefits. Comprehensive evaluations on four large, publicly available benchmarks confirm that our method surpasses state-of-the-art techniques, achieving superior performance across five evaluation metrics. Moreover, our results reveal that selecting motifs with the highest capability achieves detection performance comparable to using all heterogeneous motifs. Overall, our framework offers an effective and theoretically grounded solution for social bot detection, significantly enhancing cybersecurity measures in social networks.

cs.CR↗

arXiv2025

Machine Learning Informed by Micro and Mesoscopic Statistical Physics Methods for Community Detection

Community detection plays a crucial role in understanding the structural organization of complex networks. Previous methods, particularly those from statistical physics, primarily focus on the analysis of mesoscopic network structures and often struggle to integrate fine-grained node similarities. To address this limitation, we propose a low-complexity framework that integrates machine learning to embed micro-level node-pair similarities into mesoscopic community structures. By leveraging ensemble learning models, our approach enhances both structural coherence and detection accuracy. Experimental evaluations on artificial and real-world networks demonstrate that our framework consistently outperforms conventional methods, achieving higher modularity and improved accuracy in NMI and ARI. Notably, when ground-truth labels are available, our approach yields the most accurate detection results, effectively recovering real-world community structures while minimizing misclassifications. To further explain our framework's performance, we analyze the correlation between node-pair similarity and evaluation metrics. The results reveal a strong and statistically significant correlation, underscoring the critical role of node-pair similarity in enhancing detection accuracy. Overall, our findings highlight the synergy between machine learning and statistical physics, demonstrating how machine learning techniques can enhance network analysis and uncover complex structural patterns.

cs.SI↗

arXiv2024

Uncovering multi-order Popularity and Similarity Mechanisms in Link Prediction by graphlet predictors

Link prediction has become a critical problem in network science and has thus attracted increasing research interest. Popularity and similarity are two primary mechanisms in the formation of real networks. However, the roles of popularity and similarity mechanisms in link prediction across various domain networks remain poorly understood. Accordingly, this study used orbit degrees of graphlets to construct multi-order popularity- and similarity-based network link predictors, demonstrating that traditional popularity- and similarity-based indices can be efficiently represented in terms of orbit degrees. Moreover, we designed a supervised learning model that fuses multiple orbit-degree-based features and validated its link prediction performance. We also evaluated the mean absolute Shapley additive explanations of each feature within this model across 550 real-world networks from six domains. We observed that the homophily mechanism, which is a similarity-based feature, dominated social networks, with its win rate being 91\%. Moreover, a different similarity-based feature was prominent in economic, technological, and information networks. Finally, no single feature dominated the biological and transportation networks. The proposed approach improves the accuracy and interpretability of link prediction, thus facilitating the analysis of complex networks.

cs.SI↗

arXiv2024

The maximum capability of a topological feature in link prediction

Networks offer a powerful approach to modeling complex systems by representing the underlying set of pairwise interactions. Link prediction is the task that predicts links of a network that are not directly visible, with profound applications in biological, social, and other complex systems. Despite intensive utilization of the topological feature in this task, it is unclear to what extent a feature can be leveraged to infer missing links. Here, we aim to unveil the capability of a topological feature in link prediction by identifying its prediction performance upper bound. We introduce a theoretical framework that is compatible with different indexes to gauge the feature, different prediction approaches to utilize the feature, and different metrics to quantify the prediction performance. The maximum capability of a topological feature follows a simple yet theoretically validated expression, which only depends on the extent to which the feature is held in missing and nonexistent links. Because a family of indexes based on the same feature shares the same upper bound, the potential of all others can be estimated from one single index. Furthermore, a feature's capability is lifted in the supervised prediction, which can be mathematically quantified, allowing us to estimate the benefit of applying machine learning algorithms. The universality of the pattern uncovered is empirically verified by 550 structurally diverse networks. The findings have applications in feature and method selection, and shed light on network characteristics that make a topological feature effective in link prediction.

physics.soc-ph↗

arXiv2022

CasSeqGCN: Combining Network Structure and Temporal Sequence to Predict Information Cascades

One important task in the study of information cascade is to predict the future recipients of a message given its past spreading trajectory. While the network structure serves as the backbone of the spreading, an accurate prediction can hardly be made without the knowledge of the dynamics on the network. The temporal information in the spreading sequence captures many hidden features, but predictions based on sequence alone have their limitations. Recent efforts start to explore the possibility of combining both the network structure and the temporal feature. Here, we propose a new end-to-end prediction method CasSeqGCN in which the structure and temporal feature are simultaneously taken into account. A cascade is divided into multiple snapshots which record the network topology and the state of nodes. The graph convolutional network (GCN) is used to learn the representation of a snapshot. A novel aggregation method based on dynamic routing is proposed to aggregate node representation and the long short-term memory (LSTM) model is used to extract temporal information. CasSeqGCN predicts the future cascade size more accurately compared with other state-of-art baseline methods. The ablation study demonstrates that the improvement mainly comes from the design of the input and the GCN layer. We explicitly design an experiment to show the quality of the cascade representation learned by our approach is better than other methods. Our work proposes a new approach to combine the structural and temporal features, which not only gives a useful baseline model for future studies of cascade prediction, but also brings new insights on a wide collection of problems related with dynamics on and of the network.

cs.SI↗

arXiv2021

A novel similarity measure for mining missing links in long-path networks

Network information mining is the study of the network topology, which answers a large number of application-based questions towards the structural evolution and the function of a real system. For example, the questions can be related to how the real system evolves or how individuals interact with each other in social networks. Although the evolution of the real system may seem to be found regularly, capturing patterns on the whole process of the evolution is not trivial. Link prediction is one of the most important technologies in network information mining, which can help us understand the real system's evolution law. Link prediction aims to uncover missing links or quantify the likelihood of the emergence of nonexistent links from known network structures. Currently, widely existing methods of link prediction almost focus on short-path networks that usually have a myriad of close triangular structures. However, these algorithms on highly sparse or long-path networks have poor performance. Here, we propose a new index that is associated with the principles of Structural Equivalence and Shortest Path Length ($SESPL$) to estimate the likelihood of link existence in long-path networks. Through 548 real networks test, we find that $SESPL$ is more effective and efficient than other similarity-based predictors in long-path networks. We also exploit the performance of $SESPL$ predictor and embedding-based approaches via machine learning techniques, and the performance of $SESPL$ can achieve a gain of 44.09\% over $GraphWave$ and 7.93\% over $Node2vec$. Finally, according to the matrix of Maximal Information Coefficient ($MIC$) between all the similarity-based predictors, $SESPL$ is a new independent feature to the space of traditional similarity features.

physics.soc-ph↗

arXiv2020

A generalized linear threshold model for an improved description of the spreading dynamics

Many spreading processes in our real-life can be considered as a complex contagion, and the linear threshold (LT) model is often applied as a very representative model for this mechanism. Despite its intensive usage, the LT model suffers several limitations in describing the time evolution of the spreading. First, the discrete-time step that captures the speed of the spreading is vaguely defined. Second, the synchronous updating rule makes the nodes infected in batches, which can not take individual differences into account. Finally, the LT model is incompatible with existing models for the simple contagion. Here we consider a generalized linear threshold (GLT) model for the continuous-time stochastic complex contagion process that can be efficiently implemented by the Gillespie algorithm. The time in this model has a clear mathematical definition and the updating order is rigidly defined. We find that the traditional LT model systematically underestimates the spreading speed and the randomness in the spreading sequence order. We also show that the GLT model works seamlessly with the susceptible-infected (SI) or susceptible-infected-recovered (SIR) model. One can easily combine them to model a hybrid spreading process in which simple contagion accumulates the critical mass for the complex contagion that leads to the global cascades. Overall, the GLT model we proposed can be a useful tool to study complex contagion, especially when studying the time evolution of the spreading.

physics.soc-ph↗

arXiv2020

Measuring similarity in co-occurrence data using ego-networks

The co-occurrence association is widely observed in many empirical data. Mining the information in co-occurrence data is essential for advancing our understanding of systems such as social networks, ecosystem, and brain network. Measuring similarity of entities is one of the important tasks, which can usually be achieved using a network-based approach. Here we show that traditional methods based on the aggregated network can bring unwanted in-directed relationship. To cope with this issue, we propose a similarity measure based on the ego network of each entity, which effectively considers the change of an entity's centrality from one ego network to another. The index proposed is easy to calculate and has a clear physical meaning. Using two different data sets, we compare the new index with other existing ones. We find that the new index outperforms the traditional network-based similarity measures, and it can sometimes surpass the embedding method. In the meanwhile, the measure by the new index is weakly correlated with those by other methods, hence providing a different dimension to quantify similarities in co-occurrence data. Altogether, our work makes an extension in the network-based similarity measure and can be potentially applied in several related tasks.

cs.IR↗