Graph Neural Networks (GNNs) have emerged as a dominant paradigm for graph classification. Specifically, most existing GNNs mainly rely on the message passing strategy between neighbor nodes, where the expressivity is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Although a number of $k$-WL-based GNNs have been proposed to overcome this limitation, their computational cost increases rapidly with $k$, significantly restricting the practical applicability. Moreover, since the $k$-WL models mainly operate on node tuples, these $k$-WL-based GNNs cannot retain fine-grained node- or edge-level semantics required by attribution methods (e.g., Integrated Gradients), leading to the less interpretable problem. To overcome the above shortcomings, in this paper, we propose a novel Line Graph Aggregation Network (LGAN), that constructs a line graph from the induced subgraph centered at each node to perform the higher-order aggregation. We theoretically prove that the LGAN not only possesses the greater expressive power than the 2-WL, but also has lower time complexity under injective aggregation assumptions. Empirical evaluations on benchmarks demonstrate that the LGAN outperforms state-of-the-art $k$-WL-based GNNs, while offering better interpretability.