The maximum matching graph M（G） of a graph G is a simple graph whose vertices are the maximum matchings of G and where two maximum matchings are adjacent in M（G） if they differ by exactly one edge. In this paper, ...The maximum matching graph M（G） of a graph G is a simple graph whose vertices are the maximum matchings of G and where two maximum matchings are adjacent in M（G） if they differ by exactly one edge. In this paper, we prove that if a graph is isomorphic to its maximum matching graph, then every block of the graph is an odd cycle.展开更多
The maximum matching graph of a graph has a vertex for each maximummatching and an edge for each pair of maximum matchings which differ by exactly oneedge. In this paper, we prove that the connectivity of maximum matc...The maximum matching graph of a graph has a vertex for each maximummatching and an edge for each pair of maximum matchings which differ by exactly oneedge. In this paper, we prove that the connectivity of maximum matching graph of abipartite graph is equal to its minimum degree.展开更多
The maximum matching graph of a graph has a vertex for each maximum matching and an edge for each pair of maximum matchings which differ by exactly one edge. In this paper, we obtain a lower bound of distance between ...The maximum matching graph of a graph has a vertex for each maximum matching and an edge for each pair of maximum matchings which differ by exactly one edge. In this paper, we obtain a lower bound of distance between two vertices of maximum matching graph, and give a necessary and sufficient condition that the bound can be reached.展开更多
Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and...Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and others.The task of matching user identities across different social networks is considered a challenging task.In this work,we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data,i.e,user-name and friendship.Thus,we propose a framework,ExpandUIL,that includes three standalone al-gorithms based on(i)the percolation graph matching in Ex-pand FullName algorithm,(i)a supervised machine learning algorithm that works with the graph embedding,and(ii)a combination of the two,ExpandUserLinkage algorithm.The proposed framework as a set of algorithms is significant as,(i)it is based on the network topology and requires only name feature of the nodes,(i)it requires a considerably low initial seed,as low as one initial seed suffices,(ii)it is iterative and scalable with applicability to online incoming stream graphs,and(iv)it has an experimental proof of stability over a real ground-truth dataset.Experiments on real datasets,Instagram and VK social networks,show upto 75%recall for linked ac-counts with 96%accuracy using only one given seed pair.展开更多
基金Supported by National Natural Science of Foundation of China (Grant Nos. 10531070, 10721101)KJCX YW-S7 of CAS
文摘The maximum matching graph M（G） of a graph G is a simple graph whose vertices are the maximum matchings of G and where two maximum matchings are adjacent in M（G） if they differ by exactly one edge. In this paper, we prove that if a graph is isomorphic to its maximum matching graph, then every block of the graph is an odd cycle.
基金This research is suppouted by the National Natural Science Foundation of China(10201019)
文摘The maximum matching graph of a graph has a vertex for each maximummatching and an edge for each pair of maximum matchings which differ by exactly oneedge. In this paper, we prove that the connectivity of maximum matching graph of abipartite graph is equal to its minimum degree.
文摘The maximum matching graph of a graph has a vertex for each maximum matching and an edge for each pair of maximum matchings which differ by exactly one edge. In this paper, we obtain a lower bound of distance between two vertices of maximum matching graph, and give a necessary and sufficient condition that the bound can be reached.
文摘Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and others.The task of matching user identities across different social networks is considered a challenging task.In this work,we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data,i.e,user-name and friendship.Thus,we propose a framework,ExpandUIL,that includes three standalone al-gorithms based on(i)the percolation graph matching in Ex-pand FullName algorithm,(i)a supervised machine learning algorithm that works with the graph embedding,and(ii)a combination of the two,ExpandUserLinkage algorithm.The proposed framework as a set of algorithms is significant as,(i)it is based on the network topology and requires only name feature of the nodes,(i)it requires a considerably low initial seed,as low as one initial seed suffices,(ii)it is iterative and scalable with applicability to online incoming stream graphs,and(iv)it has an experimental proof of stability over a real ground-truth dataset.Experiments on real datasets,Instagram and VK social networks,show upto 75%recall for linked ac-counts with 96%accuracy using only one given seed pair.
