论文标题
重新审视社会渗透：从2D晶格到自适应网络
Social percolation revisited: From 2d lattices to adaptive networks
论文作者
论文摘要
社会渗透模型\ citep {Solomon-et-00}考虑了二维常规晶格。每个站点均由一个偏好$ x_ {i} $从统一分布$ u [0,1] $采样的代理占用。只有$ x_ {i} \ leq q $，代理商将有关电影质量$ Q $的信息传输给其邻居。如果$ q = q_ {c} = 0.593 $，信息会通过晶格渗透。 - 从网络角度来看，渗透群集可以看作是一个随机定型网络，具有$ n_ {c} $ nodes，而平均度取决于$ q_ {c {c} $。在确定链接概率$ p $之后，可以从$ g（n，p）$模型中生成真正的随机网络，可以从$ g（n，p）$模型中生成真正的随机网络。然后，我演示了如何将这个随机网络转换为阈值网络，在该网络中，代理创建链接取决于其$ x_ {i} $ values。假设$ x_ {i} $的动态和组形成的机制，我将模型进一步扩展到自适应社交网络模型。
The social percolation model \citep{solomon-et-00} considers a 2-dimensional regular lattice. Each site is occupied by an agent with a preference $x_{i}$ sampled from a uniform distribution $U[0,1]$. Agents transfer the information about the quality $q$ of a movie to their neighbors only if $x_{i}\leq q$. Information percolates through the lattice if $q=q_{c}=0.593$. -- From a network perspective the percolating cluster can be seen as a random-regular network with $n_{c}$ nodes and a mean degree that depends on $q_{c}$. Preserving these quantities of the random-regular network, a true random network can be generated from the $G(n,p)$ model after determining the link probability $p$. I then demonstrate how this random network can be transformed into a threshold network, where agents create links dependent on their $x_{i}$ values. Assuming a dynamics of the $x_{i}$ and a mechanism of group formation, I further extend the model toward an adaptive social network model.
