在SimBet一文中，作者给出了一个在DTN网络中利用SNA的简单例子。
如左图示，假设节点S希望向节点D发送数据。然而节点S深处一个紧密联系的小社群(clique)里。这个clique里的所有节点都从来没有见过节点D。如果采用传统的ecounter-based路由算法将很难选出合适的转发节点。
Main contribution of this paper:A new forwarding metric based on ego network analysis to locally determine a node’s centrality within the network and the node’s social similarity to the destination node.
Centrality Computation:
Centrality in graph theory and network analysis is a quantification of the relative importance of a vertex within the graph.The most widely used centrality measures are Freeman’s degree,closeness, and betweenness measures.
Degree centrality is measured as the number of direct ties that involve a given node.
Closeness centrality measures the reciprocal of the mean geodesic distance d(pi,pk), which is the shortest path between a node pi with all other reachable nodes. Closeness centrality can be regarded as a measure of how long it will take information to spread from a given node to other nodes in the network.
Betweenness centrality measures the extent to which a node lies on the paths linking other nodes. Betweenness centrality can be regarded as a measure of the extent to which a node has control over information flowing between others. A node with a high betweenness centrality has a capacity to facilitate interactions between the nodes that it links. (How about multiple geodesic paths between a single pair of nodes?)
Freeman’s centrality metrics are based on analysis of a complete and bounded network which is sometimes referred to as a socia-centric network( require complete knowledge of the network topology).
Ego-network:
Ego networks can be defined as a network consisting of a single actor ( ego ) together with the actors they are conneced to (alters) and all the links among those alters. (Here “they are” should be “it is” ?)
Degree centrality can easily be measured for an ego network where it is a simple count of the number of contacts.(Contact is temporal, I think it’s not accurate here)
Closeness centrality is uninformative in an ego network.
The betweenness centrality based on the egocentric measures does not correspond perfectly to the sociocentric measures. In effect, the betweenness value captures “how much a node connects nodes that are themselves not directly connected”.
How to compute Ego-network betweenness?
The betweenness of the ego node is the sum of the reciprocals of the entris of A2[1-A]i,j.
Similarity Computation:
Watts and Strogatz showed that real-world networks exhibit strong clustering or network transitivity.(A network is said to show "clustering” if the probability of two nodes being connected by a link is higher when the nodes in question have a common neighbour)
The degree of separation can be measured by the ratio of common neighbours between individuals in social networks.
(Scientific collaborations)The past collaborations are a good indicator of future ones.
SimBet Utility Caculation:
The SimBet utility is a value between 0 and 1 and is based on two components: similarity utility and betweenness utility.
