Graphs provide a powerful means for representing complex interactions between entities. Recently, deep learning approaches are emerging for representing and modeling graph-structured data, although the conventional deep learning methods (such as convolutional neural networks and recurrent neural networks) have mainly focused on grid-structured inputs (image and audio). Leveraged by the capability of representation learning, deep learning based techniques are reporting promising results for graph applications by detecting structural characteristics of graphs in an automated fashion. In this paper, we attempt to advance deep learning for graph-structured data by incorporating another component, transfer learning. By transferring the intrinsic geometric information learned in the source domain, our approach can help us to construct a model for a new but related task in the target domain without collecting new data and without training a new model from scratch. We thoroughly test our approach with large-scale real corpora and confirm the effectiveness of the proposed transfer learning framework for deep learning on graphs. According to our experiments, transfer learning is most effective when the source and target domains bear a high level of structural similarity in their graph representations.
@cite_18: A common and standard approach to model text document is bag-of-words. This model is suitable for capturing word frequency, however structural and semantic information is ignored. Graph representation is mathematical constructs and can model relationship and structural information effectively. A text can appropriately represented as Graph using vertex as feature term and edge relation can be significant relation between the feature terms. Text representation using Graph model provides computations related to various operations like term weight, ranking which is helpful in many applications in information retrieval. This paper presents a systematic survey of existing work on Graph based representation of text and also focused on Graph based analysis of text document for different operations in information retrieval. In this process taxonomy of Graph based representation and analysis of text document is derived and result of different methods of Graph based text representation and analysis are discussed. The survey results shows that Graph based representation is appropriate way of representing text document and improved result of analysis over traditional model for different text applications.
@cite_21: Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (, 2009), which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences. The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence-based models (e.g., LSTMs) when the problem is graph-structured. We demonstrate the capabilities on some simple AI (bAbI) and graph algorithm learning tasks. We then show it achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.
@cite_1: How much did a network change since yesterday? How different is the wiring between Bob's brain (a left-handed male) and Alice's brain (a right-handed female)? Graph similarity with known node correspondence, i.e. the detection of changes in the connectivity of graphs, arises in numerous settings. In this work, we formally state the axioms and desired properties of the graph similarity functions, and evaluate when state-of-the-art methods fail to detect crucial connectivity changes in graphs. We propose DeltaCon, a principled, intuitive, and scalable algorithm that assesses the similarity between two graphs on the same nodes (e.g. employees of a company, customers of a mobile carrier). Experiments on various synthetic and real graphs showcase the advantages of our method over existing similarity measures. Finally, we employ DeltaCon to real applications: (a) we classify people to groups of high and low creativity based on their brain connectivity graphs, and (b) do temporal anomaly detection in the who-emails-whom Enron graph.
@cite_8: 
Graphs can provide a general way of representing the diverse interactions of entities and have been studied extensively @cite_18 . In addition to studies on representation and quantification of relations and similarities @cite_1 , various studies focused on large-scale graph data and use of structural information. Recently, deep learning methods to automatically extract structural characteristics from graphs have been proposed @cite_8 @cite_21 .