all nodes of the system. However, social networks are usually not constructed for the purpose of sending messages between unrelated individuals. Thus, there should be a fundamental origin that governs the formation of the spatial scaling law, Eq. (1). Here we suggest that the origin of this scaling, Eq. (1), comes from a general perspective based on the concept of entropy. We hypothesize that human social behavior is based on gathering maximum information through di ff erent activities. Making friends can be regarded as a way of collecting information. To get optimal information could be a general purpose for an individual that shapes the social network architecture. We will show that a social network based on Eq. (1) is an optimal network which can benefit people in collecting m aximal information. To model a social system we use a toroidal lattice to denote the world in which each node represents an individual. We assume that each individual has a finite energy w which can be represented by the sum of distances between an individual and all his or her friends, I. MODEL m ∑ v = 1 r ( u , v ) = w , (2) where m is the number of direct links of node u . Eq. (2) implies that every node u selects its long range acquaintances v , one by one, until the total distance reaches w . The information that node v brings to u can be evaluated by considering the information of node v and all its neighbors. Thus, the information that u collects can be expressed by the sequence of nodes as illustrated in Fig. 1 and the entropy of the whole sequence measures the amount of information. We assume that all nodes are equivalent, so the information obtained by one node can represent the information obtained by each of the other nodes. Thus, our model for constructing a social network is Max ε = − n ∑ i = 1 q$_{i}$ log q$_{i}$ , (3) subjected to Eq. (2). In Eq. (3), q$_{i}$ denotes the frequency of node i in the information sequence (see Fig. 1) and n is the size of the network. When i is not a neighbor and not a next nearest neighbor of u , q$_{i}$ = 0, and we define q$_{i}$ log q$_{i}$ = 0. Here, Eq. (3) implies that the information entropy ε is determined by the sequence of friends and friends of friends (For considering also friends of next nearest friends, see supplementary IIA). 3