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Publications (6)
Graph neural networks (GNNs) have witnessed significant adoption in the industry owing to impressive performance on various predictive tasks. Performance alone, however, is not enough. Any widely deployed machine learning algorithm must be robust to adversarial attacks. In this work, we investigate this aspect for GNNs, identify vulnerabilities, an...
In signed networks, each edge is labeled as either positive or negative. The edge sign captures the polarity of a relationship. Balance of signed networks is a well-studied property in graph theory. In a balanced (sub)graph, the vertices can be partitioned into two subsets with negative edges present only across the partitions. Balanced portions of...
Language networks have been proposed to be the underlying representation for syntactic knowledge (Roelofs, 1992; Pickering and Branigan, 1998). Such networks are known to explain various word order related priming effects in psycholinguistics. Under the assumption that word order information is encoded in these networks, we explore if Greenbergian...