Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, wemore »
Discovering functionality of urban regions by learning low-dimensional representations of a spatial multiplex network
The complex relationships in an urban environment can be captured through multiple interrelated sources of data. These relationships form multilayer networks, that are also spatially embedded in an area, could be used to identify latent patterns. In this work, we propose a low-dimensional representation learning approach that considers multiple layers of a multiplex network simultaneously and is able to encode similarities between nodes across different layers. In particular, we introduce a novel neural network architecture to jointly learn low-dimensional representations of each network node from multiple layers of a network. This process simultaneously fuses knowledge of various data sources to better capture the characteristics of the nodes. To showcase the proposed method we focus on the problem of identifying the functionality of an urban region. Using a variety of public data sources for New York City, we design a multilayer network and evaluate our approach. Our results indicate that our proposed approach can improve the accuracy of traditional approaches in an unsupervised task.
- Award ID(s):
- 1739413
- Publication Date:
- NSF-PAR ID:
- 10074570
- Journal Name:
- Proceedings of the Third Mining Urban Data Workshop (MUD 2018)
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Networked data involve complex information from multifaceted channels, including topology structures, node content, and/or node labels etc., where structure and content are often correlated but are not always consistent. A typical scenario is the citation relationships in scholarly publications where a paper is cited by others not because they have the same content, but because they share one or multiple subject matters. To date, while many network embedding methods exist to take the node content into consideration, they all consider node content as simple flat word/attribute set and nodes sharing connections are assumed to have dependency with respect to allmore »
-
Learning low-dimensional representations of graphs has facilitated the use of traditional machine learning techniques to solving classic network analysis tasks such as link prediction, node classification, community detection, etc. However, to date, the vast majority of these learning tasks are focused on traditional single-layer/unimodal networks and largely ignore the case of multiplex networks. A multiplex network is a suitable structure to model multi-dimensional real-world complex systems. It consists of multiple layers where each layer represents a different relationship among the network nodes. In this work, we propose MUNEM, a novel approach for learning a low-dimensional representation of a multiplex networkmore »
-
Time-continuous dimensional descriptions of emotions (e.g., arousal, valence) allow researchers to characterize short-time changes and to capture long-term trends in emotion expression. However, continuous emotion labels are generally not synchronized with the input speech signal due to delays caused by reaction-time, which is inherent in human evaluations. To deal with this challenge, we introduce a new convolutional neural network (multi-delay sinc network) that is able to simultaneously align and predict labels in an end-to-end manner. The proposed network is a stack of convolutional layers followed by an aligner network that aligns the speech signal and emotion labels. This network ismore »
-
Abstract Dynamic community detection provides a coherent description of network clusters over time, allowing one to track the growth and death of communities as the network evolves. However, modularity maximization, a popular method for performing multilayer community detection, requires the specification of an appropriate null network as well as resolution and interlayer coupling parameters. Importantly, the ability of the algorithm to accurately detect community evolution is dependent on the choice of these parameters. In functional temporal networks, where evolving communities reflect changing functional relationships between network nodes, it is especially important that the detected communities reflect any state changes ofmore »