In real-world applications of influence maximization (IM), the network structure is often unknown. Thus, we may identify the most influential seed nodes by exploring only a part of the underlying network given a small budget for node queries. Motivated by the fact that collecting node metadata is more cost-effective than investigating the relationship between nodes via queried nodes, we propose IM-META, an end-to-end solution to IM in networks with unknown topology by retrieving information from queries and node metadata. However, using such metadata to aid the IM process is not without risk due to the noisy nature of metadata and uncertainties in connectivity inference. To tackle these challenges, 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 model, 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. By querying only 5% of nodes, IM-META reaches 93% of the upper bound performance.

翻译：在影响最大化的现实应用(IM)中,网络结构往往不为人知。 因此, 我们可能通过只探索基础网络的一部分,为节点查询提供少量预算,来确定最有影响力的种子节点。 我们的动机是,收集节点元比通过查询节点调查节点之间的关系更具成本效益。 我们建议IM-META, 一种在具有未知地形的网络中通过检索查询和节点元数据获得信息,对IM的端到端解决方案。 然而, 使用这类元数据帮助IM进程并非没有风险, 因为元数据的吵闹性质和连通性推断中的不确定性。 为了应对这些挑战, 我们制定了一个新的IMM问题, 目的是找到种子节点并查询节点。 在IM-META中, 我们开发了一种有效的方法, 通过Siames神经网络模型, 我们学习所收集的元数据与边缘之间的关系, 2 我们选择了用来构建强化的图表的假设边缘, 以及 3) 我们确定下一个节点, 通过使用我们上层MET- META 的顶部排序, 仅通过我们上层排序 5 来最大化的推算。