Allahverdyan A E, Steeg G Ver, Galstyan A
Yerevan Physics Institute, Alikhanian Brothers Street 2, Yerevan 375036, Armenia.
USC Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, California 90292, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Dec;92(6):062824. doi: 10.1103/PhysRevE.92.062824. Epub 2015 Dec 22.
We study a mechanism of activity sustaining on networks inspired by a well-known model of neuronal dynamics. Our primary focus is the emergence of self-sustaining collective activity patterns, where no single node can stay active by itself, but the activity provided initially is sustained within the collective of interacting agents. In contrast to existing models of self-sustaining activity that are caused by (long) loops present in the network, here we focus on treelike structures and examine activation mechanisms that are due to temporal memory of the nodes. This approach is motivated by applications in social media, where long network loops are rare or absent. Our results suggest that under a weak behavioral noise, the nodes robustly split into several clusters, with partial synchronization of nodes within each cluster. We also study the randomly weighted version of the models where the nodes are allowed to change their connection strength (this can model attention redistribution) and show that it does facilitate the self-sustained activity.
我们研究了一种受著名神经元动力学模型启发的网络活动维持机制。我们主要关注的是自我维持集体活动模式的出现，在这种模式下，没有单个节点能够独自保持活跃，但最初提供的活动会在相互作用的主体群体中持续存在。与由网络中存在的（长）回路导致的现有自我维持活动模型不同，这里我们关注树状结构，并研究由于节点的时间记忆而产生的激活机制。这种方法的动机来自于社交媒体中的应用，在社交媒体中长网络回路很少见或不存在。我们的结果表明，在弱行为噪声下，节点会稳健地分裂成几个簇，每个簇内的节点部分同步。我们还研究了模型的随机加权版本，其中允许节点改变其连接强度（这可以模拟注意力重新分配），并表明这确实有助于自我维持活动。
