Graph Representation Learning

Top PDF Graph Representation Learning:

Sequence-to-sequence modeling for graph representation learning

Sequence-to-sequence modeling for graph representation learning

There is still a lack of well-performing approaches for learning the representation for an entire graph. There are several challenges that need to be addressed within this area. First, the choice of the subgraph structures to be incorporated in the graph representation learning has a significant impact on the expressiveness power of the embeddings of an entire graph. Second, choosing the appropriate granularity level of this substructure (e.g., whether to include first or second order neighborhoods of a node when building node sequences), which is necessary to preserve the graph embedding, is an open problem. The choice may depend on many factors, such as the graph domain, scale, density, and its various structural properties. The types of the substructures, from fine-to-coarse, such as nodes, edges, trees, graphlets, random walks, and communities, can capture local and global features of the graph. The question is what types of substructures with what level of granularity are informative enough to capture the general graph structure and recognize similarity between graphs, while reducing the loss of information? The additional chal- lenge is, of course, the efficiency of learning the representation of the substructures and aggregating them into a graph embedding. In this work, we investigate these challenges within the context of our proposed architectures.
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Accurate Text Enhanced Knowledge Graph Representation Learning

Accurate Text Enhanced Knowledge Graph Representation Learning

Previous representation learning techniques for knowledge graph representation usually represent the same entity or relation in differ- ent triples with the same representation, with- out considering the ambiguity of relations and entities. To appropriately handle the semantic variety of entities/relations in distinct triples, we propose an accurate text-enhanced knowl- edge graph representation learning method, which can represent a relation/entity with dif- ferent representations in different triples by ex- ploiting additional textual information. Specif- ically, our method enhances representations by exploiting the entity descriptions and triple- specific relation mention. And a mutual atten- tion mechanism between relation mention and entity description is proposed to learn more accurate textual representations for further improving knowledge graph representation. Experimental results show that our method achieves the state-of-the-art performance on both link prediction and triple classification tasks, and significantly outperforms previous text-enhanced knowledge representation mod- els.
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LinkNBed: Multi Graph Representation Learning with Entity Linkage

LinkNBed: Multi Graph Representation Learning with Entity Linkage

Many data driven organizations such as Google and Microsoft take the approach of constructing a unified super-graph by integrating data from multi- ple sources. Such unification has shown to signifi- cantly help in various applications, such as search, question answering, and personal assistance. To this end, there exists a rich body of work on linking entities and relations, and conflict resolution (e.g., knowledge fusion (Dong et al., 2014). Still, the problem remains challenging for large scale knowl- edge graphs and this paper proposes a deep learning solution that can play a vital role in this construc- tion process. In real-world setting, we envision our method to be integrated in a large scale system that would include various other components for tasks like conflict resolution, active learning and human-in-loop learning to ensure quality of con- structed super-graph. However, we point out that our method is not restricted to such use cases—one can readily apply our method to directly make infer- ence over multiple graphs to support applications like question answering and conversations.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Therefore, recent methods, especially those on headline generation, require a profound understanding of the natural language system characteristics that represent the information in documents. Understanding these characteristics involves identifying the morphology in a particular sentence structure and information on sentence syntax and syntax formulas that must be used to generate a perfect sentence. Comprehending the characteristics of the natural language stem, the natural language processing technique, and the machine learning technique allows the development of intelligent headline generation techniques. Subsequently, these techniques are expected to execute the generation task perfectly and produce results similar to those generated by humans.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

The k-means algorithm is used to recognize data into different classes (known as clusters). This unsupervised learning algorithm is widely used in sensor node clustering problem due to its linear complexity and simple implementation [10].Loo et al. [11], present an intrusion detection scheme for sensor networks based on anomaly detection. They use a fixed width clustering algorithm to allow for the detection of previously unseen attacks. They also came up with 12 general features for detecting sinkholes and periodic route error attacks. Generally -means is used to detect novel intrusions in WSN by dividing or clustering the network connection’s data to collect the majority of the intrusions together in one or several clusters, the figure below present the K-means clustering algorithm:
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Intermittent errors in signature databases, in the code of the antivirus tool or in file compression and encryption algorithms used frequently used by antivirus tools are the main cause [r]

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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Initially Trapezoidal Notch band monopole antenna is constructed from the basic design of trapezoidal monopole wideband antenna and the corresponding antenna parameters and Radiation cha[r]

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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

With the most flexible in operation, mobile ad hoc networks (MANETs) are increasingly gaining higher amount of reception with respect to next-generation network arena. Also with the increasing mobility one of the key issues to be addressed is the anomaly detection rate. Anomaly-detection [13] based on dynamic learning process was designed to perform the process of identifying the intrusion at specific time intervals using multidimensional statistics. However, security remained unaddressed. To provide security, a fuzzy model was introduced in [14] by increasing the identification of intrusion rate. But, classification of normal and abnormal activities was not performed. Separate classification of normal and abnormal activities was concentrated on [15] with the help of proactive and reactive protocol. An enhanced protocol called as the Secured AODV (SAODV) was introduced in [16] using a Trust Based Mechanism (TBM) to improve the throughput level.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

• Aspect-oriented software development: Aspect-oriented software development (AOSD) is a new approach to software development that addresses limitations inherent in other approaches, including object-oriented programming. AOSD aims to address crosscutting concerns by providing means for systematic identification, separation, representation and composition. Crosscutting concerns are encapsulated in separate modules, known as aspects, so that localization can be promoted. This results in better support for modularization hence reducing development, maintenance and evolution costs [27].
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

!"| | # (2) where E is the expected value. NL equidistant samples are taken from equation 2 to find the discrete representation of PAPR, $ is known as the oversampling factor. It is shown that L = 4 is a good value to have accurate results of PAPR for simulation purposes [2]. Discrete version of Equation 2 has the following mathematical form:

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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Nowadays data extracted from various or large database are transformed into meaningful structure. This transformed structure is used for various purpose and its powerful and produces an intended result. The size and difficulty of performances in the datasets are increased. The KDD(knowledge discovery dataset) are cleaning the missing values ,inconsistent and incomplete data, integrating multiple values ,selecting the relevant data, transforming to suitable format, knowledge extraction from intelligence ,some interesting measures or thresholds are applied and exact pattern returned ,presentation in graph trees etc.. The above KDD are represented in many ways. More functionality is also used. Clustering is used by many applications. It is said to be an attractive task in data mining. The major uses of clustering are marketing ,land use ,insurance, city planning and earth –quake
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Can define ontology as a set of domain concepts interest of the organization representing the hierarchical structure. Where [17] defined ontology, as a catalogue of the kinds of things that are supposed to exist in the area. Ontology helps to know what it means for a specific term. Ontologies provide a way to describe the meaning of the terms and relationships so that a common understanding or consensus can be obtained between machines and people [18]. A number of studies have dealt with this type of text representation. For instance, In [11], the authors proposed a system that utilizes the concept of weight for text clustering. It was developed with a k-means algorithm based on the principle of ontology. The system is used to identify irrelevant or redundant features that may reduce strength, thereby achieving more accurate document clustering.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

USER SEGMENT OF KOREAN WIDE AREA GLOBAL NAVIGATION SATELLITE SYSTEM SAYED CHHATTAN SHAH Assistant Professor Department of Information Communications Engineering Hankuk University of Fore[r]

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A Graph Kernel based on Jensen-Shannon Representation

A Graph Kernel based on Jensen-Shannon Representation

Experimental Results: In terms of the classification accu- racies, our JSMK kernel can easily outperform all the alter- native graph kernels on any dataset. The classification accu- racies of our JSMK kernel are obviously higher than those of all the alternative kernels. The reasons for the effective- ness are threefold. First, compared to the WLSK, SPGK, GCGK and JTQK kernels that require decomposing graphs into substructures, our JSMK kernel can establish the sub- structure location correspondence which is not considered in these kernels. Second, compared to the JSGK and QJSK ker- nels that rely on the similarity measure between global graphs in terms of the classical or quantum JSD, our JSMK kernel can identify the correspondence information between both the vertices and the substructures, and can thus reflect richer in- terior topological characteristics of graphs. By contrast, the JSGK and QJSK kernels can only reflect the global similarity information between graphs. Third, compared to the DBMK kernel that can also reflect the correspondence information between substructures, our JSMK kernel can identify more pairs of aligned isomorphic substructures. Moreover, as we have stated in Section 3.3, the m-layer JS representation can reflect richer characteristics than the h-layer DB representa- tion. As a result, the JSMK kernel using the JS representation can capture more information for graphs than the DBMK ker- nel using the DB representation.
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Image matching using relational graph representation

Image matching using relational graph representation

The methodology is consists of six steps: (1) input image, (2) line segment extraction from the image, (3) the interpretation and derivation of structural descriptions from the line-ex[r]

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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Table 4.5 shows that four path relations representing four hypotheses were significant. Graphical image of paths is presented in figure 4.1 and 4.2 .The results of boot strapping method (Table 4.5) show a p-value for each relation. All structural model relationships were significant considering a p-value = 0.05. In the model all IV’s had a significant a positive coefficients which means, companies with higher level of BA will tend to achieve a better SC performance. Among BA dimensions the highest coefficient belonged to Plan (β=0.268, p<0.05) followed by Source (β=0.253, p<0.05) and Make (β=0.258, p<0.05). Compare to the other BA components delivery had a lower influence on SC performance (β=0.436, p<0.05). It is important to note that contrary to confirmative SEM models (e.g., LISREL), explorative PLS models still do not have such global indicators that would assess the overall goodness of the model, to evaluate the goodness of fit for models. The criterion of global fitness (GoF) was calculated. The GoF is a geometric average of all communalities and R2 in the model. The GoF is an index that can be used to validate models with PLS. The R2 coefficient is 0.628, which demonstrates that the indicator of analytical businesses was able to explain 62.8% of the variability in the performance results. A value higher than the GoF> 0.5 shows that the set of structural equations is well defined and offers a good representation of the dataset and is valid. GoF of current model was 0.647 which is ready to consider 64.7 % of the reachable fitness.
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Context dependent random walk graph kernels and tree pattern graph matching kernels with applications to action recognition

Context dependent random walk graph kernels and tree pattern graph matching kernels with applications to action recognition

effectively combines context-dependent graph kernels of different orders. In our tree-pattern graph matching kernel, more topological structural information is exploited. We have recursively computed the similarity between affinal tree-pattern groups in a dynamic programming formulation and applied a sparse constraint to match the tree pattern groups. The errors caused by falsely matched affinal tree-pattern groups are suppressed and the discriminative power of the tree pattern graph matching is increased. We have applied the proposed kernels to recognize human actions by constructing the concurrent graph and the causal graph to capture the spatiotemporal relations among local feature vectors. Experimental results on several datasets have demonstrated that the two graphs for representing actions are complementary and the proposed context-dependent random walk graph kernel and tree-pattern graph matching kernel are effective at improving the performance of action recognition. Our tree pattern graph matching kernel yields more accurate results than our context-dependent random walk kernel.
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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

PROPOSED METHOD In this journal we perform a comparison using three methods which are clustering algorithms, Fuzzy C-Means FCM, Standard K-means SKM and Enhanced K-Means EKM.. After we p[r]

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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

ABSTRACT The real-time hardware application is developed around a FPGA hardware architecture that includes embedded processor MicroBlaze on the field programmable gate array FPGA.This pa[r]

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GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

GRAPH BASED TEXT REPRESENTATION FOR DOCUMENT CLUSTERING

Algorithm Shortest Path Using Candidates mainly uses the matrices Reverse Matrix, Weighted Graph Matrix, and Mark Matrix in Figures 4, 5, and 7 respectively. The Reverse Matrix is generated from original unweighted directed Graph Matrix representation in Fig. 2. The two given source and destination nodes are assumed to exist in the graph G. An efficient algorithm called Path Existence Query that aids in finding the existence of the path in a directed graph from <s> to <t> is presented in ref. [1]. The algorithm proceeds by finding the candidate nodes starting from the destination node <t> visiting all predecessors towards the source node <s>. This is the main advantage of using the reverse representation in Fig. 4. Marking nodes is possible by updating Mark Matrix as shown in Fig. 7. It starts by initializing marked vertices to unmark tag equals to zero. Then the algorithm finds the shortest path among the marked nodes as of Weighted Graph Matrix representation. The function keeps in each entry of the main values; Vertex, Dist, and Pred Node. These values are updated as the procedure proceeds. Specifically, it starts from the source node <s> checking the marked nodes and calculating the path distance horizontally then diagonally. The function stores in Dist (when first visit the node) the accumulated
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