53 citations · 141 across the 16 of their papers we have counts for
17 papers
Learning Graph ARMA Processes from Time-Vertex Spectra
Eylem Tugce Guneyi, Berkay Yaldiz, Abdullah Canbolat +1
The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation pa…
Learning Multi-Modal Nonlinear Embeddings: Performance Bounds and an Algorithm
Semih Kaya, Elif Vural
While many approaches exist in the literature to learn low-dimensional representations for data collections in multiple modalities, the generalizability of multi-modal nonlinear em…
Graph Domain Adaptation with Localized Graph Signal Representations
Yusuf Yigit Pilavci, Eylem Tugce Guneyi, Cemil Cengiz +1
In this paper we propose a domain adaptation algorithm designed for graph domains. Given a source graph with many labeled nodes and a target graph with few or no labeled nodes, we…
Mask Combination of Multi-layer Graphs for Global Structure Inference
Eda Bayram, Dorina Thanou, Elif Vural +1
Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph st…
Domain Adaptation on Graphs by Learning Graph Topologies: Theoretical Analysis and an Algorithm
Elif Vural
Traditional machine learning algorithms assume that the training and test data have the same distribution, while this assumption does not necessarily hold in real applications. Dom…
Domain Adaptation on Graphs by Learning Aligned Graph Bases
Mehmet Pilanci, Elif Vural
A common assumption in semi-supervised learning with graph models is that the class label function varies smoothly on the data graph, resulting in the rather strict prior that the…