Summary: Time-varying graphs are increasingly common in financial, social and biological data analysis applications. Feature extraction that efficiently encodes the complex structure of sparse, multi-layered, dynamic graphs presents computational and methodological challenges. In the past decade, topological data analysis has become a popular method of studying the shape of data. This is achieved by building an increasing sequence of simplicial complexes (called filtration) indexed by a scale parameter on top of the data to keep track of topological changes along with the filtration. This multi-scale summary, called persistence diagram (PD), is often vectorized to be used in machine learning algorithms. This paper introduces a topological approach to extract information on higher-order interactions encoded in persistence diagrams from graph data. Our framework has two main steps: first, we convert the graph into a higher-dimensional simplicial complex by adding structures such as triangles, tetrahedrons etc., and compute a PD using the so-called lower-star filtration which utilizes quantitative node attributes. Then, we vectorize the PD by averaging the associated Betti function over successive scale values of a one-dimensional grid using integration. A notable aspect of our procedure is that it avoids embedding a graph into a metric space. We show that the proposed vectorization summary is robust against input noise with respect to the \(L_1\) 1-Wasserstein distance. In simulation studies, the proposed approach leads to improved change point detection rates and outperforms one of the state-of-the-art methods for anomaly detection in time-varying graphs. In real data application, our approach leads to up to a 20% gain in anomalous price prediction in the Ethereum cryptocurrency transaction network.