Title: Multi-task Network Embedding with Adaptive Loss Weighting. Abstract: Network embedding is to learn low-dimensional representations of nodes which mostly preserve the network topological structure. In real-world networks, however, nodes are often associated with a rich set of attributes and labels which are potentially valuable in seeking more effective vector representations. To properly utilize this information, we propose a Joint Autoencoders framework for Multi-task network Embedding (JAME), which aims to encode a shared representation of local network structure, node attributes, and available node labels. Jointly em-bedding via multi-task learning is strongly dependent on the relative weighting between each task’s loss function. Tuning these weights by hand is an expensive and difficult process, making multi-task learning prohibitive in practice. Therefore, we define an adaptive loss weighting layer capable of learning an optimal combination of loss weights during representation learning. Empirical evaluations on real-world datasets show effectiveness and efficiency of our JAME model compared to relevant baseline methods.