Representation learning of dynamic networks

• URL: http://arxiv.org/abs/2412.11065v1
• Date: Sun, 15 Dec 2024 05:45:20 GMT
• Title: Representation learning of dynamic networks
• Authors: Haixu Wang, Jiguo Cao, Jian Pei,
• Abstract summary: This study presents a novel representation learning model tailored for dynamic networks. We address (i) attribute learning, (ii) community detection, and (iii) link prediction and recovery of individual nodes in the dynamic network. We validated our approach through simulation studies and real-world applications.
• Score: 23.63765745034283
• License:
• Abstract: This study presents a novel representation learning model tailored for dynamic networks, which describes the continuously evolving relationships among individuals within a population. The problem is encapsulated in the dimension reduction topic of functional data analysis. With dynamic networks represented as matrix-valued functions, our objective is to map this functional data into a set of vector-valued functions in a lower-dimensional learning space. This space, defined as a metric functional space, allows for the calculation of norms and inner products. By constructing this learning space, we address (i) attribute learning, (ii) community detection, and (iii) link prediction and recovery of individual nodes in the dynamic network. Our model also accommodates asymmetric low-dimensional representations, enabling the separate study of nodes' regulatory and receiving roles. Crucially, the learning method accounts for the time-dependency of networks, ensuring that representations are continuous over time. The functional learning space we define naturally spans the time frame of the dynamic networks, facilitating both the inference of network links at specific time points and the reconstruction of the entire network structure without direct observation. We validated our approach through simulation studies and real-world applications. In simulations, we compared our methods link prediction performance to existing approaches under various data corruption scenarios. For real-world applications, we examined a dynamic social network replicated across six ant populations, demonstrating that our low-dimensional learning space effectively captures interactions, roles of individual ants, and the social evolution of the network. Our findings align with existing knowledge of ant colony behavior.

Related papers

• Contrastive Representation Learning for Dynamic Link Prediction in Temporal Networks [1.9389881806157312]
  We introduce a self-supervised method for learning representations of temporal networks. We propose a recurrent message-passing neural network architecture for modeling the information flow over time-respecting paths of temporal networks. The proposed method is tested on Enron, COLAB, and Facebook datasets.
  arXiv  Detail & Related papers  (2024-08-22T22:50:46Z)
• Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes [11.494631894700253]
  We introduce ODE Processes for Network Dynamics (NDP4ND), a new class of processes governed by data-adaptive network dynamics. We show that the proposed method has excellent data and computational efficiency, and can adapt to unseen network emerging dynamics.
  arXiv  Detail & Related papers  (2023-10-25T08:44:05Z)
• Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations [0.0]
  Piecewise-Veable Model (PiVeM) for representation of continuous-time dynamic networks. We show that PiVeM can successfully represent network structure and dynamics in ultra-low two-dimensional spaces. It outperforms relevant state-of-art methods in downstream tasks such as link prediction.
  arXiv  Detail & Related papers  (2022-12-23T13:57:56Z)
• Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation [55.80128181112308]
  We show that dimensionality and quasi-orthogonality of neural networks' feature space may jointly serve as network's performance discriminants. Our findings suggest important relationships between the networks' final performance and properties of their randomly initialised feature spaces.
  arXiv  Detail & Related papers  (2022-03-30T21:47:32Z)
• Data-driven emergence of convolutional structure in neural networks [83.4920717252233]
  We show how fully-connected neural networks solving a discrimination task can learn a convolutional structure directly from their inputs. By carefully designing data models, we show that the emergence of this pattern is triggered by the non-Gaussian, higher-order local structure of the inputs.
  arXiv  Detail & Related papers  (2022-02-01T17:11:13Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• Learning Asynchronous and Sparse Human-Object Interaction in Videos [56.73059840294019]
  Asynchronous-Sparse Interaction Graph Networks (ASSIGN) is able to automatically detect the structure of interaction events associated with entities in a video scene. ASSIGN is tested on human-object interaction recognition and shows superior performance in segmenting and labeling of human sub-activities and object affordances from raw videos.
  arXiv  Detail & Related papers  (2021-03-03T23:43:55Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Network Classifiers Based on Social Learning [71.86764107527812]
  We propose a new way of combining independently trained classifiers over space and time. The proposed architecture is able to improve prediction performance over time with unlabeled data. We show that this strategy results in consistent learning with high probability, and it yields a robust structure against poorly trained classifiers.
  arXiv  Detail & Related papers  (2020-10-23T11:18:20Z)
• Deep learning of contagion dynamics on complex networks [0.0]
  We propose a complementary approach based on deep learning to build effective models of contagion dynamics on networks. By allowing simulations on arbitrary network structures, our approach makes it possible to explore the properties of the learned dynamics beyond the training data. Our results demonstrate how deep learning offers a new and complementary perspective to build effective models of contagion dynamics on networks.
  arXiv  Detail & Related papers  (2020-06-09T17:18:34Z)
• Link Prediction for Temporally Consistent Networks [6.981204218036187]
  Link prediction estimates the next relationship in dynamic networks. The use of adjacency matrix to represent dynamically evolving networks limits the ability to analytically learn from heterogeneous, sparse, or forming networks. We propose a new method of canonically representing heterogeneous time-evolving activities as a temporally parameterized network model.
  arXiv  Detail & Related papers  (2020-06-06T07:28:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.