Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given multimillion-node graphs such as "who-follows-whom", "patent-cites-patent", "user-likes-page" and "actor/director-makes-movie" networks, how can  find unexpected behaviors? When companies operate on the graphs with monetary incentives to sell Twitter "Follors" and Facebook page "Likes", the graphs show strange connectivity patterns. ,  study a complete graph from a large Twitter-style social network, spanning up to 3.33 billion edges.  report strange deviations from typical patterns like smooth degree distributions.  find that such deviations are often due to "lockstep behavior" that large groups of follors connect to the same groups of folloes. Our first contribution is that  study strange patterns on the adjacency matrix and in the spectral subspaces with respect to several flavors of lockstep.  discover that (a) the lockstep behaviors on the graph shape dense "block" in its adjacency matrix and creates "rays" in spectral subspaces, and (b) partially overlapping of the behaviors shape "staircase" in its adjacency matrix and creates "pearls" in spectral subspaces. The second contribution is that  provide a fast algorithm, using the discovery as a guide for practitioners, to detect users who offer the lockstep behaviors in undirected/directed/bipartite graphs.  carry out extensive experiments on both synthetic and real datasets, as ll as public datasets from IMDb and US Patent. The results demonstrate the scalability and effectiveness of our proposed algorithm.
['Meng Jiang', 'Peng Cui', 'Alex Beutel', 'Christos Faloutsos', 'Shiqiang Yang']
I am writing a research paper on a idea Inferring lockstep behavior from connectivity pattern in large graphs
[{"from": "human", "value": "I am writing a research paper on a idea Inferring lockstep behavior from connectivity pattern in large graphs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Given multimillion-node graphs such as \"who-follows-whom\", \"patent-cites-patent\", \"user-likes-page\" and \"actor/director-makes-movie\" networks, how can  find unexpected behaviors? When companies operate on the graphs with monetary incentives to sell Twitter \"Follors\" and Facebook page \"Likes\", the graphs show strange connectivity patterns. ,  study a complete graph from a large Twitter-style social network, spanning up to 3.33 billion edges.  report strange deviations from typical patterns like smooth degree distributions.  find that such deviations are often due to \"lockstep behavior\" that large groups of follors connect to the same groups of folloes. Our first contribution is that  study strange patterns on the adjacency matrix and in the spectral subspaces with respect to several flavors of lockstep.  discover that (a) the lockstep behaviors on the graph shape dense \"block\" in its adjacency matrix and creates \"rays\" in spectral subspaces, and (b) partially overlapping of the behaviors shape \"staircase\" in its adjacency matrix and creates \"pearls\" in spectral subspaces. The second contribution is that  provide a fast algorithm, using the discovery as a guide for practitioners, to detect users who offer the lockstep behaviors in undirected/directed/bipartite graphs.  carry out extensive experiments on both synthetic and real datasets, as ll as public datasets from IMDb and US Patent. The results demonstrate the scalability and effectiveness of our proposed algorithm."}]