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Found 14 papers, 4 papers with code
UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance
In this paper, we push forward the state-of-the-art performance of unsupervised MOT methods by proposing UnsMOT, a novel framework that explicitly combines the appearance and motion features of objects with geometric information to provide more accurate tracking.
Federated Few-shot Learning for Cough Classification with Edge Devices
In this work, our aim is to develop a framework that can effectively perform cough classification even in situations when enormous cough data is not available, while also addressing privacy concerns.
Node Feature Augmentation Vitaminizes Network Alignment
Network alignment (NA) is the task of discovering node correspondences across multiple networks.
A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty
In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks.
Grad-Align+: Empowering Gradual Network Alignment Using Attribute Augmentation
Network alignment (NA) is the task of discovering node correspondences across different networks.
META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks
The discovery of community structures in social networks has gained considerable attention as a fundamental problem for various network analysis tasks.
On the Power of Gradual Network Alignment Using Dual-Perception Similarities
Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes.
IM-META: Influence Maximization Using Node Metadata in Networks With Unknown Topology
Since the structure of complex networks is often unknown, we may identify the most influential seed nodes by exploring only a part of the underlying network, given a small budget for node queries.
Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs.
An Improved Approach for Estimating Social POI Boundaries With Textual Attributes on Social Media
It has been insufficiently explored how to perform density-based clustering by exploiting textual attributes on social media.
DeepNC: Deep Generative Network Completion
In this paper, we present DeepNC, a novel method for inferring the missing parts of a network based on a deep generative model of graphs.
Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model
As collaborative filtering (CF) is one of the most prominent and popular techniques used for recommender systems, we propose a new clustering-based CF (CBCF) method using an incentivized/penalized user (IPU) model only with ratings given by users, which is thus easy to implement.
DIR-ST$^2$: Delineation of Imprecise Regions Using Spatio--Temporal--Textual Information
To overcome these problems, we present DIR-ST$^2$, a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio--textual information but also temporal information on social media.
Community Detection in Partially Observable Social Networks
The discovery of community structures in social networks has gained significant attention since it is a fundamental problem in understanding the networks' topology and functions.
