Since the structure of complex networks is often unknown, we may identify the most influential seed nodes by exploring only a part of the underlying network, given a small budget for node queries. We propose IM-META, a solution to influence maximization (IM) in networks with unknown topology by retrieving information from queries and node metadata. Since using such metadata is not without risk due to the noisy nature of metadata and uncertainties in connectivity inference, we formulate a new IM problem that aims to find both seed nodes and queried nodes. In IM-META, we develop an effective method that iteratively performs three steps: 1) we learn the relationship between collected metadata and edges via a Siamese neural network, 2) we select a number of inferred confident edges to construct a reinforced graph, and 3) we identify the next node to query by maximizing the inferred influence spread using our topology-aware ranking strategy. Through experimental evaluation of IM-META on four real-world datasets, we demonstrate a) the speed of network exploration via node queries, b) the effectiveness of each module, c) the superiority over benchmark methods, d) the robustness to more difficult settings, e) the hyperparameter sensitivity, and f) the scalability.

翻译：摘要：由于复杂网络的结构通常未知，我们可在有限的节点查询预算下，通过仅探索底层网络的一部分来识别最具影响力的种子节点。我们提出IM-META，一种通过从查询和节点元数据中检索信息来应对未知拓扑网络中的影响力最大化问题的解决方案。由于元数据存在噪声特性及连通性推断中的不确定性，使用此类元数据并非没有风险，因此我们定义了一个新的影响力最大化问题，旨在同时寻找种子节点和查询节点。在IM-META中，我们开发了一种有效方法，迭代执行三个步骤：1）通过孪生神经网络学习收集到的元数据与边之间的关系；2）选择若干推断出的可靠边以构建增强图；3）利用我们提出的基于拓扑感知的排序策略，通过最大化推断的影响力传播来确定下一个待查询节点。通过在四个真实数据集上对IM-META进行实验评估，我们展示了：a)节点查询中网络探索的速度，b)每个模块的有效性，c)相较于基准方法的优越性，d)在更困难设置下的鲁棒性，e)超参数敏感性，以及f)可扩展性。