Networks are natural but powerful structures that capture relationships between different entities in many domains, such as social networks, citation networks, bioinformatic networks. In many applications that require multiple networks analysis, network alignment, the task of recognizing node correspondence across different networks, plays an important role. A wellknown application of network alignment is to identify which accounts in different social networks belong to the same person. Given the appeal of network alignment, there is a rich body of researches that aims to tackle this problem. However, many research challenges still exist, such as enhancing the accuracy and improving the scalability due to the information explosion. With such motivation, in scope of our PhD work, we address the three crucial challenges in network alignment literature, namely (i) enhancing scalability of network alignment on large-scale graphs, (ii) enhancing the robustness of network alignment to adversarial conditions and (iii) multi-modal information integration for network aligners. To do so, we focus on proposing aligner frameworks for different types of input attributed networks from simple to complex. Each framework attempts to answer simultaneously all three research questions by leveraging embedding techniques, where the input networks are embedded into insightful, low-dimensional vector spaces. This helps to enrich the nodes’ individual context with multi-modal information, thus facilitates the distinction between nodes. The learnt embeddings also enables faster alignment retrieval by direct vector comparison. Our proposed techniques improve upon the state-of-the-art for different types of attributed networks and cover a large range of applications.