We are living in an ever more connected world, where data recording the interactions between people, software systems, and the physical world is becoming increasingly prevalent. This data often takes the form of a temporally evolving graph, where entities are the vertices and the interactions between them are the edges. We call such graphs interaction graphs. Various application domains, including telecommunications, transportation, and social media, depend on analytics performed on interaction graphs. The ability to efficiently support historical analysis over interaction graphs require effective solutions for the problem of data layout on disk. This paper presents an adaptive disk layout called the railway layout for optimizing disk block storage for interaction graphs. The key idea is to divide blocks into one or more sub-blocks, where each sub-block contains a subset of the attributes, but the entire graph structure is replicated within each sub-block. This improves query I/O, at the cost of increased storage overhead. We introduce optimal ILP formulations for partitioning disk blocks into sub-blocks with overlapping and non-overlapping attributes. Additionally, we present greedy heuristic approaches that can scale better compared to the ILP alternatives, yet achieve close to optimal query I/O. To demonstrate the benefits of the railway layout, we provide an extensive experimental study comparing our approach to a few baseline alternatives.