论文标题
在有限的网络信息下影响最大化：播种高度邻居
Influence maximization under limited network information: Seeding high-degree neighbors
论文作者
论文摘要
可以通过迫使少数种子个体首先采用的信息，规范和实践的扩散。先前工作中提出的策略要么假设全网络信息或对收集哪些信息的大大控制。但是，互联网上的隐私设置和调查中的高无响应通常严重限制了可用的连接信息。在这里，我们提出了一个有限网络信息的场景播种策略：仅知道某些随机节点的程度和连接。这种新策略是对“随机邻居采样”的修改，并播种了随机选择节点的最高程度邻居。在一系列合成和现实世界网络上的线性阈值模型的模拟中，我们发现这种新策略的表现优于其他种子策略，包括高度播种和聚类的播种。
The diffusion of information, norms, and practices across a social network can be initiated by compelling a small number of seed individuals to adopt first. Strategies proposed in previous work either assume full network information or large degree of control over what information is collected. However, privacy settings on the Internet and high non-response in surveys often severely limit available connectivity information. Here we propose a seeding strategy for scenarios with limited network information: Only the degrees and connections of some random nodes are known. This new strategy is a modification of "random neighbor sampling" and seeds the highest-degree neighbors of randomly selected nodes. In simulations of a linear threshold model on a range of synthetic and real-world networks, we find that this new strategy outperforms other seeding strategies, including high-degree seeding and clustered seeding.
