Zhu, Jiong, Jin, Junchen, Loveland, Donald, Schaub, Michael T., and Koutra, Danai. How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. Retrieved from https://par.nsf.gov/biblio/10406940. ACM SIGKDD Conference on Knowledge Discovery and Data Mining . Web. doi:10.1145/3534678.3539418.

Zhu, Jiong, Jin, Junchen, Loveland, Donald, Schaub, Michael T., & Koutra, Danai. How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications. ACM SIGKDD Conference on Knowledge Discovery and Data Mining, (). Retrieved from https://par.nsf.gov/biblio/10406940. https://doi.org/10.1145/3534678.3539418

Zhu, Jiong, Jin, Junchen, Loveland, Donald, Schaub, Michael T., and Koutra, Danai. "How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications". ACM SIGKDD Conference on Knowledge Discovery and Data Mining (). Country unknown/Code not available. https://doi.org/10.1145/3534678.3539418. https://par.nsf.gov/biblio/10406940.

@article{osti_10406940,
place = {Country unknown/Code not available}, title = {How does Heterophily Impact the Robustness of Graph Neural Networks?: Theoretical Connections and Practical Implications}, url = {https://par.nsf.gov/biblio/10406940}, DOI = {10.1145/3534678.3539418}, abstractNote = {}, journal = {ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, author = {Zhu, Jiong and Jin, Junchen and Loveland, Donald and Schaub, Michael T. and Koutra, Danai}, }