Effectively capturing graph node sequences in the form of vector embeddings is critical to many applications. We achieve this by (i) first learning vector embeddings of single graph nodes and (ii) then composing them to compactly represent node sequences. Specifically, we propose SENSE-S (Semantically Enhanced Node Sequence Embedding -for Single nodes), a skip-gram based novel embedding mechanism, for single graph nodes that co-learns graph structure as well as their textual descriptions. We demonstrate that SENSE-S vectors increase the accuracy of multi-label classification tasks by up to 50% and link-prediction tasks by up to 78% under a variety of scenarios using real datasets. Based on SENSE-S, we next propose generic SENSE to compute composite vectors that represent a sequence of nodes, where preserving the node order is important. We prove that this approach is efficient in embedding node sequences, and our experiments on real data confirm its high accuracy in node order decoding.
This paper presents two approaches: one called SENSE-S for embedding nodes in attributed networks; the other one called SENSE for embedding a sequence of nodes. SENSE-S follows the structure of Skip-gram model. The main difference is that SENSE-S considers both node and words in node content as input and output for learning their embedding. For generating embedding vector for a sequence of nodes, SENSE takes summation of cyclically shifted unit-vectors constructed by SENSE-S on nodes in a sequence.    
The paper is well written with a clear definition of the studied problem and a clear introduction of the presented methods. Evaluation was conducted on two real-world data sets (Wikipedia and citation network). It is an interesting idea to represent a sequence by the summation of cyclically shifted unit-vectors of nodes in a sequence. However, there are several concerns about the work presented in this paper. 
1) the evaluation of SENSE-S is not sufficient. The baseline methods used in comparison are the simple ones that take concatenation of vectors induced from text and graph, or use one for initializing the learning of the other.  There existing several approaches that learn node embedding vectors from attributed graph (considering both the node content text and graph topology structure), such as TADW [1], HSCA [2], PLANE [3],GAE[4], AANE[5], ANRL [6]. SENSE-S should be compared with these methods for showing its effectiveness. 
2) the embedding vector of a node sequence is evaluated by showing the decoding accuracy. It would be more interesting to show how these vectors can be used for some real applications. And, to have high decoding accuracy, the embedding dimension for sequences of 10 nodes should be up to 1024, which is quite expensive for computing and for storage, making the presented method unpractical in real-world applications.   
[1] C. Yang, Z. Liu, D. Zhao, M. Sun, E. Y. Chang, Network representation learning with rich text information. IJCAI, 2015
[2] D. Zhang, J. Yin, X. Zhu, C. ZHang, Homophily, structure, and content augmented network representation learning.  ICDM 2016. 
[3] T. M. V. Le and H. W. Lauw. Probabilistic latent document network embedding.  ICDM, 2014.
[4] Thomas N Kipf, Max Welling. Variational Graph Auto-Encoders. NIPS Workshop on Bayesian Deep Learning.  2016
[5] Xiao Huang, Jundong Li, Xia Hu. Accelerated attributed network embedding. SDM 2017.
[6] Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, Can Wang. ANRL: Attributed Network Representation Learning via Deep Neural Networks. IJCAI, 2018