Waqas Nawaz

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Graph summarization is valuable approach to analyze various real life phenomenon, like communities, influential nodes, and information flow in a big graph. To summarize a graph, nodes having similar neighbors are merged into super nodes and their corresponding edges are compressed into super edges. Existing methods find similar nodes either by nodes(More)
Assorted networks have transpired for analysis and visualization, including social community network, biological network, sensor network and many other information networks. Prior approaches either focus on the topological structure or attribute likeness for graph clustering. A few recent methods constituting both aspects however cannot be scalable with(More)
Email service providers have employed many email classification and prioritization systems over the last decade to improve their services. In order to assist email services, we propose a personalized email community detection method to discover the groupings of email users based on their structural and semantic intimacy. We extract the personalized social(More)
An abundance of structural information has resulted in non-trivial graph traversals. Shortcut construction is among the utilized techniques implemented for efficient shortest path (SP) traversals on graphs. However, shortcut construction, being a computationally intensive task, required to be exclusive and offline, often produces unnecessary auxiliary data,(More)
In this paper we combine the neighborhood and attributes similarity to summarize big graphs where each node is attached with multiple attributes. The main intution behind our approach is that sets of nodes having common links, in graphs, usually have same attributes. Thus compressing such Sets of Similar Nodes (SSNs) can significantly reduce the size of big(More)
Graph is an extremely versatile data structure in terms of its expressiveness and flexibility to model a range of real life phenomenon. Various networks like social networks, sensor networks and computer networks are represented and stored in the form of graphs. The analysis of these kind of graphs has an immense importance from quite a long time. It is(More)
The restoration process of many known median based algorithms is effective for the images corrupted by high random valued impulse noise, but not efficient especially for real-time applications. We proposed a new modus-operandi; by utilizing the competence of the fast median filter into modified Directional Weighted Median filter (DWM), which can be used in(More)
The shortest path traversal queries require to scan the entire graph. The repetitive scans make it hard to answer the traversal queries in a reasonable time. Many traversal algorithms imply precomputed information to speed up the run-time query process. However, computing effective auxiliary data at pre-processing stage is still an active problem. It is(More)
The shortest path problem is among the most fundamental combinatorial optimization problems to answer reach-ability queries. A single pair shortest path computation using Dijkstra’s algorithm requires few seconds on a very large social network. Many traversal algorithms imply precomputed information to speed up the runtime query process. However, computing(More)
Dense subgraph discovery, in a large graph, is useful to solve the community search problem. Motivated from this, we propose a graph summarization method where we search and aggregate dense subgraphs into super nodes. Since the dense subgraphs have high overlap of common neighbors, thus merging such subgraphs can produce a highly compact summary graph.(More)