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Sushi rechavia dim sam menu may12 heb
Sushi rechavia dim sam menu may12 heb
weiss2001
Â
Below are the important points I note from the 2020 paper by Martin Grohe: - 1-WL distinguishes almost all graphs, in a probabilistic sense - Classical WL is two dimensional Weisfeiler-Leman - DeepWL is an unlimited version of WL graph that runs in polynomial time. - Knowledge graphs are essentially graphs with vertex/edge attributes ABSTRACT: Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...
Subhajit Sahu
Â
https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1 There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing). Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution. To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution. In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution. The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion. Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
Subhajit Sahu
Â
My notes on shared memory parallelism. Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].
Shared memory Parallelism (NOTES)
Shared memory Parallelism (NOTES)
Subhajit Sahu
Â
**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include: - Random walk methods - Spectral partitioning - Label propagation - Greedy agglomerative and divisive algorithms - Clique percolation https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6 There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*. **Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community. Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity. Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**. The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.
A Dynamic Algorithm for Local Community Detection in Graphs : NOTES
A Dynamic Algorithm for Local Community Detection in Graphs : NOTES
Subhajit Sahu
Â
A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_. https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are: - Vertex following and Minimum label - Data caching - Graph coloring - Threshold scaling With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well. From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.
Scalable Static and Dynamic Community Detection Using Grappolo : NOTES
Scalable Static and Dynamic Community Detection Using Grappolo : NOTES
Subhajit Sahu
Â
This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time. https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0 Communities can be of the following **types**: - Disjoint - Overlapping - Hierarchical - Local. The following **static** community detection **methods** exist: - Spectral-based - Statistical inference - Optimization - Dynamics-based The following **dynamic** community detection **methods** exist: - Independent community detection and matching - Dependent community detection (evolutionary) - Simultaneous community detection on all snapshots - Dynamic community detection on temporal networks **Applications** of community detection include: - Criminal identification - Fraud detection - Criminal activities detection - Bot detection - Dynamics of epidemic spreading (dynamic) - Cancer/tumor detection - Tissue/organ detection - Evolution of influence (dynamic) - Astroturfing - Customer segmentation - Recommendation systems - Social network analysis (both) - Network summarization - Privary, group segmentation - Link prediction (both) - Community evolution prediction (dynamic, hot field) <br> <br> ## References - [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)
Application Areas of Community Detection: A Review : NOTES
Application Areas of Community Detection: A Review : NOTES
Subhajit Sahu
Â
This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram. Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex. https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329
Community Detection on the GPU : NOTES
Community Detection on the GPU : NOTES
Subhajit Sahu
Â
Recommended
Sushi rechavia dim sam menu may12 heb
Sushi rechavia dim sam menu may12 heb
weiss2001
Â
Below are the important points I note from the 2020 paper by Martin Grohe: - 1-WL distinguishes almost all graphs, in a probabilistic sense - Classical WL is two dimensional Weisfeiler-Leman - DeepWL is an unlimited version of WL graph that runs in polynomial time. - Knowledge graphs are essentially graphs with vertex/edge attributes ABSTRACT: Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view. Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...
Subhajit Sahu
Â
https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1 There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing). Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution. To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution. In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution. The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion. Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
Subhajit Sahu
Â
My notes on shared memory parallelism. Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].
Shared memory Parallelism (NOTES)
Shared memory Parallelism (NOTES)
Subhajit Sahu
Â
**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include: - Random walk methods - Spectral partitioning - Label propagation - Greedy agglomerative and divisive algorithms - Clique percolation https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6 There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*. **Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community. Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity. Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**. The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.
A Dynamic Algorithm for Local Community Detection in Graphs : NOTES
A Dynamic Algorithm for Local Community Detection in Graphs : NOTES
Subhajit Sahu
Â
A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_. https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are: - Vertex following and Minimum label - Data caching - Graph coloring - Threshold scaling With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well. From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.
Scalable Static and Dynamic Community Detection Using Grappolo : NOTES
Scalable Static and Dynamic Community Detection Using Grappolo : NOTES
Subhajit Sahu
Â
This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time. https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0 Communities can be of the following **types**: - Disjoint - Overlapping - Hierarchical - Local. The following **static** community detection **methods** exist: - Spectral-based - Statistical inference - Optimization - Dynamics-based The following **dynamic** community detection **methods** exist: - Independent community detection and matching - Dependent community detection (evolutionary) - Simultaneous community detection on all snapshots - Dynamic community detection on temporal networks **Applications** of community detection include: - Criminal identification - Fraud detection - Criminal activities detection - Bot detection - Dynamics of epidemic spreading (dynamic) - Cancer/tumor detection - Tissue/organ detection - Evolution of influence (dynamic) - Astroturfing - Customer segmentation - Recommendation systems - Social network analysis (both) - Network summarization - Privary, group segmentation - Link prediction (both) - Community evolution prediction (dynamic, hot field) <br> <br> ## References - [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)
Application Areas of Community Detection: A Review : NOTES
Application Areas of Community Detection: A Review : NOTES
Subhajit Sahu
Â
This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram. Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex. https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329
Community Detection on the GPU : NOTES
Community Detection on the GPU : NOTES
Subhajit Sahu
Â
Useful additions to inbuilt child_process module. đŚ Node.js, đ Files, đ° Docs. Please see attached PDF for literature survey. https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e
Survey for extra-child-process package : NOTES
Survey for extra-child-process package : NOTES
Subhajit Sahu
Â
For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May. https://gist.github.com/wolfram77/692d263f463fd49be6eb5aa65dd4d0f9
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER
Subhajit Sahu
Â
For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May. https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...
Subhajit Sahu
Â
In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later. https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a
Fast Incremental Community Detection on Dynamic Graphs : NOTES
Fast Incremental Community Detection on Dynamic Graphs : NOTES
Subhajit Sahu
Â
1. Human population didn't explode, but plateued. 2. Fertilizer prices are going to the sky. 3. Farmers are looking for alternatives such as animal waste (manure) or even human waste. 4. Manure prices are also going up. 5. Switching to organic farming not an option. https://gist.github.com/wolfram77/49067fc3ddc1ba2e1db4f873056fd88a
Can you ďŹx farming by going back 8000 years : NOTES
Can you ďŹx farming by going back 8000 years : NOTES
Subhajit Sahu
Â
1. Webpages tend to behave as authorities or hubs. 2. An authority represents an research thesis, and a hub represents an encyclopedia. 3. Each page has an authority and a hub score. 4. The graph is based on query, included pointed to and from pages. 5. Authority score is the sum of scores of all hubs pointing to it. 6. Hub score is the sum of scores of all authorities is pointing to. 7. Score are normalized with L2-norm in each iteration (root of sum of squares). 8. Needs to be performed at query time. 9. Two scores are returned, instead of just one. https://gist.github.com/wolfram77/3d9ef6c5a5b63f53caabce4812c7ea81
HITS algorithm : NOTES
HITS algorithm : NOTES
Subhajit Sahu
Â
Computer architectures are facing issues: Memory latencies are far higher. Benefits from instruction level parallelism (ILP) is reducing. With increasing clock rates, power consumption is increasing. Increasing complexity with multi-stage pipelines, intermediate buffers, multi-level caches, out-of-order execution, branch prediction, ... GPUs are parallel computer architectures that are good at some tasks, not so good at others. Running routines with high arithmetic intensity with overlapped memory access is the preferred approach. They may be unsuitable for irregular algorithms, where it is difficult to get high efficiency due to the high latency of accesses. They are less versatile compared to CPUs, using SIMD parallelism, and are dense compute-wise (per currency). NVIDIA's CUDA programming model enables GPUs to be used for general-purpose computing, and hence the term GPGPU. GPU Architectural, Programming, and Performance Models presentation at PPoPP, 2010, Bangalore, India. By Prof. Kishore Kothapalli with Prof. P. J. Narayanan and Suryakant Patidar. https://gist.github.com/wolfram77/43a6660121eef45b78c10d4e652dad6c
Basic Computer Architecture and the Case for GPUs : NOTES
Basic Computer Architecture and the Case for GPUs : NOTES
Subhajit Sahu
Â
For the IPDPS ParSocial event a presentation submission is required by 15th May. The event is on 3rd June. https://gist.github.com/wolfram77/51b15ca09eb28f6909673a2deb1a314d DYNAMIC BATCH PARALLEL ALGORITHMS FOR UPDATING PAGERANK Subhajit Sahut, Kishore Kothapallit and Dip Sankar Banerjeet tInternational Institute of Information Technology Hyderabad, India. tIndian Institute of Technology Jodhpur, India. subhajit.sahu@research. ,kkishore@iiit.ac.in, dipsankarb@iitj.ac.in This work is partially supported by a grant from the Department of Science and Technology (DST), India, under the National Supercomputing Mission (NSM) R&D in Exascale initiative vide Ref. No: DST/NSM/R&D Exascale/2021/16. FACEBOOK 15 TAKING A PAGE OUT OF GOOGLEâS PLAYBOOK 10 STOP FAKE NEWS FROM GOING VIRAL PUBLISHED APR 2015 BY SALVADOR RODRIGUEZ Click-Gap: When is Facebook is driving disproportionate amounts of traffic to websites. Effort to rid fakes news from Facebookâs services. Is a website relying on Facebook to drive significant traffic, but not well ranked by the rest of the web? Also News Citation Graph. PAGERANK APPLICATIONS Ranking of websites. Measuring scientific impact of researchers. Finding the best teams and athletes. Ranking companies by talent concentration. Predicting road/foot traffic in urban spaces. Analysing protein networks. Finding the most authoritative news sources Identifying parts of brain that change jointly. Toxic waste management. PAGERANK APPLICATIONS Debugging complex software systems (Moni torRank) Finding the most original writers (BookRank) Finding topical authorities (TwitterRank) WHAT IS PAGERANK lâ-d Plu = Cus + ââ UCIiNny Pru u->v = (1-âd) x âus ( ) outdegy, PageRank is a lLink-analysis algorithm. By Larry Page and Sergey Brin in 1996. For ordering information on the web. Represented with a random-surfer model. Rank of a page is defined recursively. Calculate iteratively with power-iteration.
Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES
Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES
Subhajit Sahu
Â
Satellites are usually covered in aluminized polyimide. The yellowish gold color of polyimide with silver aluminium side facing in gives the satellite the appearance of being wrapped in gold. The material is called Multi-layer Insulation (MLI). It helps in radiative insulation of the onboard instruments of satellite. Gold is actually used in electrical contacts to prevent corrosion due to Ultra-violet light or X-rays. https://gist.github.com/wolfram77/8ae2de1a29caf1a2f84babed79943389
Are Satellites Covered in Gold Foil : NOTES
Are Satellites Covered in Gold Foil : NOTES
Subhajit Sahu
Â
This tutorial discusses on tax calculation for long-term and short-term capital gains, as well as for business income that speculative (intraday, BTST) and non-speculative (F&O, BTST). Also a nice concept on tax-loss harvesting. When turnover for business income is more than 5Cr or gains less than 6% an audit is required by a CA. Business income requires maintaining balance sheet and income statement. I may need an audit by a CA.
Taxation for Traders < Markets and Taxation : NOTES
Taxation for Traders < Markets and Taxation : NOTES
Subhajit Sahu
Â
This paper discusses a method of Generalizing PageRank algorithm for different types of networks. Rank of each vertex is considered to be dependent upon both the in- and out-edges. Each edge can also have differing importance. This solves the problem of dead ends and spider traps without the need of taxation (?). --- Abstractâ PageRank is a well-known algorithm that has been used to understand the structure of the Web. In its classical formulation the algorithm considers only forward looking paths in its analysis- a typical web scenario. We propose a generalization of the PageRank algorithm based on both out-links and in-links. This generalization enables the elimination network anomalies- and increases the applicability of the algorithm to an array of new applications in networked data. Through experimental results we illustrate that the proposed generalized PageRank minimizes the effect of network anomalies, and results in more realistic representation of the network. Keywords- Search Engine; PageRank; Web Structure; Web Mining; Spider-Trap; dead-end; Taxation;Web spamming
A Generalization of the PageRank Algorithm : NOTES
A Generalization of the PageRank Algorithm : NOTES
Subhajit Sahu
Â
With biomedical signal processing algorithms, such as the Pan-Tompkins QRS peak detection algorithm, FIR filters are utilized. Raw ECG signal can be fed to a Moving window filter, which helps filter out noise and get the signal of interest. These FIR filters involve the use of multipliers and adders, which take in several input sample and output a single sample. This paper replaces accurate adders in such filters with 10 16-bit signed approximate adders (power ve error parameters) from the EvoApprox library. Functional validation is done in MATLAB with Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of Moving Window Integration; and Mean Square Error (MSE) of thresholds as error metrics. MIT-BIH Arrythmia database is used as the raw ECG input. In hardware evaluation, a 100-point FIR filter is implemented with a single Multiply-accumulate unit where the exact adder is replaced with selected approximate adder. RTL model is synthesized with 45nm NandGate Open Cell library in Synopsys design compiler. Area, Average power, and Worst-case delay are measured. On average the presented methodology provides an area-saving of 19.71% and power-saving of 19.27%.
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...
Subhajit Sahu
Â
1. Old vs New tax regime [Form26AS] 2. Quarterly Tax Deducted at Source (TDS) [Form16] 3. Quarterly Tax Deducted at Source (other income) [Form 15G/H] 4. Quarterly Advance Tax (extra income) [Challan ITNS 280] Extras: - Public Provident Fund scheme (PPF) - National Pension Scheme (NPS) References: - https://www.incometax.gov.in/iec/foportal - https://finshots.in/archive/finshots-money-resolution-4-axe-your-tax/
Income Tax Calender 2021 (ITD) : NOTES
Income Tax Calender 2021 (ITD) : NOTES
Subhajit Sahu
Â
Groceries, nutrition kits, vegetables, women sanitation kits, heavy rain relief, awareness sessions, and a lot of help to people in need, including children.
Youngistaan Foundation: Annual Report 2020-21 : NOTES
Youngistaan Foundation: Annual Report 2020-21 : NOTES
Subhajit Sahu
Â
5 Activites were performed by members of Youngistaan Foundation in order to help increase voting turnup for Telangana 2018 elections, among the youth of Hyderabad.
Youngistaan: Voting awarness-campaign : NOTES
Youngistaan: Voting awarness-campaign : NOTES
Subhajit Sahu
Â
Highlighted notes on: Cost Efficient PageRank Computation using GPU This paper discusses the use of Aitken extrapolated Power method for PageRank computation. However, the results are unclear whether the performance improvement is due to GPU implementation, or due to use of Aitken extrapolation. The paper mentions a good performance improvement for damping factor values close to 1, and very low tolerance values which are usually not used for PageRank computation. It needs to be cross-checked to see if Aitken extrapolation provides and reduction of iterations on CPU only (as the same effect would be observed on the GPU, only timings change).
Cost Efficient PageRank Computation using GPU : NOTES
Cost Efficient PageRank Computation using GPU : NOTES
Subhajit Sahu
Â
This is my report on: Rank adjustment strategies for Dynamic PageRank (v1). While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli. Abstract â To avoid calculating ranks of vertices in a dynamic graph from scratch for every snapshot, the ones computed in the previous snapshot of the graph can be used, with adjustment. Four different rank adjustment strategies for dynamic PageRank are studied here. These include zero-fill, 1/N-fill, scaled zero-fill, and scaled 1/N-fill. Results indicate that the scaled 1/N-fill strategy requires the least number of iterations, on average. As long as the graph has no affected dead ends (including dead ends in the previous snapshot), unaffected vertices can be skipped with this adjustment strategy. Index terms â PageRank algorithm, Dynamic graph, Rank adjustment, Initial ranks.
Rank adjustment strategies for Dynamic PageRank : REPORT
Rank adjustment strategies for Dynamic PageRank : REPORT
Subhajit Sahu
Â
Highlighted notes on: Variadic CRTP by Steve Dewhurst (28 March 2017) Thus is a very good method of using CRTP for separation of concerns. The CuriouslyâRecurring Template Pattern (CRTP) is a pleasant and common C++ coding idiom. Basically itâs a way for a class to inherit a selfâcustomized capability.
Variadic CRTP : NOTES
Variadic CRTP : NOTES
Subhajit Sahu
Â
Highlighted notes on: Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2009-2013) The objective of this report is to present a list of proceedings (conferences, workshops, symposia, meetings) in the areas of Computer Science, Electrical & Electronic Engineering, and Communications covered by Google Scholar Metrics and ranked according to their hindex. Google Scholar Metrics only displays publications that have published at least 100 papers and have received at least one citation in the last five years (2009-2013). The searches were conducted between the 15th and 22nd of December, 2014. A total of 1208 proceedings have been identified.
Proceedings Scholar Metrics : NOTES
Proceedings Scholar Metrics : NOTES
Subhajit Sahu
Â
Highlighted notes on: Top CSE conferences list (IIIT Hyderabad) ICDE: International Conference on Data Engineering WWW: International World Wide Web Conference VLDB: International Conference on Very Large Databases ICDM: International Conference on Data Mining ICDE: International Conference on Data Engineering WSDM: International Conference on Web Search and Data Mining POPL: ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages PLDI: ACM SIGPLAN Conference on Programming Language Design and Implementation OOPSLA: Conference on Object Oriented Programming Systems Languages and Applications PODC: ACM Symposium on Principles of Distributed Computing SODA: ACM/SIAM Symposium on Discrete Algorithms ASPLOS: Architectural Support for Programming Languages and Operating Systems SC: ACM/IEEE Supercomputing Conference IPDPS: International Parallel and Distributed Processing Symposium SIGMETRICS: ACM SIG on Computer and Communication Metrics and Performance ICSE: International Conference on Software Engineering FSE/ESEC: ACM SIGSOFT International Symposium on Foundations of Software Engineering
Top CSE conferences list (IIIT Hyderabad) : NOTES
Top CSE conferences list (IIIT Hyderabad) : NOTES
Subhajit Sahu
Â
Slides held at the Collaboration Summit 2024 on Microsoft365 Development Security
Microsoft365_Dev_Security_2024_05_16.pdf
Microsoft365_Dev_Security_2024_05_16.pdf
Markus Moeller
Â
In this 24-minute webinar we discuss the practical approaches companies can take to effectively automate their test execution. In particular we will look at the evolution of test automation frameworks, the pros and cons of building your own test grid and the pros and cons of buying a commercial test grid, so you can make the right decision for the needs of you business. Watch the video at https://www.element34.com/assets/test-automation-buy-vs-build-strategic-impact
The Strategic Impact of Buying vs Building in Test Automation
The Strategic Impact of Buying vs Building in Test Automation
Element34
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Useful additions to inbuilt child_process module. đŚ Node.js, đ Files, đ° Docs. Please see attached PDF for literature survey. https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e
Survey for extra-child-process package : NOTES
Survey for extra-child-process package : NOTES
Subhajit Sahu
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For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May. https://gist.github.com/wolfram77/692d263f463fd49be6eb5aa65dd4d0f9
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER
Subhajit Sahu
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For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May. https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...
Subhajit Sahu
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In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later. https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a
Fast Incremental Community Detection on Dynamic Graphs : NOTES
Fast Incremental Community Detection on Dynamic Graphs : NOTES
Subhajit Sahu
Â
1. Human population didn't explode, but plateued. 2. Fertilizer prices are going to the sky. 3. Farmers are looking for alternatives such as animal waste (manure) or even human waste. 4. Manure prices are also going up. 5. Switching to organic farming not an option. https://gist.github.com/wolfram77/49067fc3ddc1ba2e1db4f873056fd88a
Can you ďŹx farming by going back 8000 years : NOTES
Can you ďŹx farming by going back 8000 years : NOTES
Subhajit Sahu
Â
1. Webpages tend to behave as authorities or hubs. 2. An authority represents an research thesis, and a hub represents an encyclopedia. 3. Each page has an authority and a hub score. 4. The graph is based on query, included pointed to and from pages. 5. Authority score is the sum of scores of all hubs pointing to it. 6. Hub score is the sum of scores of all authorities is pointing to. 7. Score are normalized with L2-norm in each iteration (root of sum of squares). 8. Needs to be performed at query time. 9. Two scores are returned, instead of just one. https://gist.github.com/wolfram77/3d9ef6c5a5b63f53caabce4812c7ea81
HITS algorithm : NOTES
HITS algorithm : NOTES
Subhajit Sahu
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Computer architectures are facing issues: Memory latencies are far higher. Benefits from instruction level parallelism (ILP) is reducing. With increasing clock rates, power consumption is increasing. Increasing complexity with multi-stage pipelines, intermediate buffers, multi-level caches, out-of-order execution, branch prediction, ... GPUs are parallel computer architectures that are good at some tasks, not so good at others. Running routines with high arithmetic intensity with overlapped memory access is the preferred approach. They may be unsuitable for irregular algorithms, where it is difficult to get high efficiency due to the high latency of accesses. They are less versatile compared to CPUs, using SIMD parallelism, and are dense compute-wise (per currency). NVIDIA's CUDA programming model enables GPUs to be used for general-purpose computing, and hence the term GPGPU. GPU Architectural, Programming, and Performance Models presentation at PPoPP, 2010, Bangalore, India. By Prof. Kishore Kothapalli with Prof. P. J. Narayanan and Suryakant Patidar. https://gist.github.com/wolfram77/43a6660121eef45b78c10d4e652dad6c
Basic Computer Architecture and the Case for GPUs : NOTES
Basic Computer Architecture and the Case for GPUs : NOTES
Subhajit Sahu
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For the IPDPS ParSocial event a presentation submission is required by 15th May. The event is on 3rd June. https://gist.github.com/wolfram77/51b15ca09eb28f6909673a2deb1a314d DYNAMIC BATCH PARALLEL ALGORITHMS FOR UPDATING PAGERANK Subhajit Sahut, Kishore Kothapallit and Dip Sankar Banerjeet tInternational Institute of Information Technology Hyderabad, India. tIndian Institute of Technology Jodhpur, India. subhajit.sahu@research. ,kkishore@iiit.ac.in, dipsankarb@iitj.ac.in This work is partially supported by a grant from the Department of Science and Technology (DST), India, under the National Supercomputing Mission (NSM) R&D in Exascale initiative vide Ref. No: DST/NSM/R&D Exascale/2021/16. FACEBOOK 15 TAKING A PAGE OUT OF GOOGLEâS PLAYBOOK 10 STOP FAKE NEWS FROM GOING VIRAL PUBLISHED APR 2015 BY SALVADOR RODRIGUEZ Click-Gap: When is Facebook is driving disproportionate amounts of traffic to websites. Effort to rid fakes news from Facebookâs services. Is a website relying on Facebook to drive significant traffic, but not well ranked by the rest of the web? Also News Citation Graph. PAGERANK APPLICATIONS Ranking of websites. Measuring scientific impact of researchers. Finding the best teams and athletes. Ranking companies by talent concentration. Predicting road/foot traffic in urban spaces. Analysing protein networks. Finding the most authoritative news sources Identifying parts of brain that change jointly. Toxic waste management. PAGERANK APPLICATIONS Debugging complex software systems (Moni torRank) Finding the most original writers (BookRank) Finding topical authorities (TwitterRank) WHAT IS PAGERANK lâ-d Plu = Cus + ââ UCIiNny Pru u->v = (1-âd) x âus ( ) outdegy, PageRank is a lLink-analysis algorithm. By Larry Page and Sergey Brin in 1996. For ordering information on the web. Represented with a random-surfer model. Rank of a page is defined recursively. Calculate iteratively with power-iteration.
Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES
Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES
Subhajit Sahu
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Satellites are usually covered in aluminized polyimide. The yellowish gold color of polyimide with silver aluminium side facing in gives the satellite the appearance of being wrapped in gold. The material is called Multi-layer Insulation (MLI). It helps in radiative insulation of the onboard instruments of satellite. Gold is actually used in electrical contacts to prevent corrosion due to Ultra-violet light or X-rays. https://gist.github.com/wolfram77/8ae2de1a29caf1a2f84babed79943389
Are Satellites Covered in Gold Foil : NOTES
Are Satellites Covered in Gold Foil : NOTES
Subhajit Sahu
Â
This tutorial discusses on tax calculation for long-term and short-term capital gains, as well as for business income that speculative (intraday, BTST) and non-speculative (F&O, BTST). Also a nice concept on tax-loss harvesting. When turnover for business income is more than 5Cr or gains less than 6% an audit is required by a CA. Business income requires maintaining balance sheet and income statement. I may need an audit by a CA.
Taxation for Traders < Markets and Taxation : NOTES
Taxation for Traders < Markets and Taxation : NOTES
Subhajit Sahu
Â
This paper discusses a method of Generalizing PageRank algorithm for different types of networks. Rank of each vertex is considered to be dependent upon both the in- and out-edges. Each edge can also have differing importance. This solves the problem of dead ends and spider traps without the need of taxation (?). --- Abstractâ PageRank is a well-known algorithm that has been used to understand the structure of the Web. In its classical formulation the algorithm considers only forward looking paths in its analysis- a typical web scenario. We propose a generalization of the PageRank algorithm based on both out-links and in-links. This generalization enables the elimination network anomalies- and increases the applicability of the algorithm to an array of new applications in networked data. Through experimental results we illustrate that the proposed generalized PageRank minimizes the effect of network anomalies, and results in more realistic representation of the network. Keywords- Search Engine; PageRank; Web Structure; Web Mining; Spider-Trap; dead-end; Taxation;Web spamming
A Generalization of the PageRank Algorithm : NOTES
A Generalization of the PageRank Algorithm : NOTES
Subhajit Sahu
Â
With biomedical signal processing algorithms, such as the Pan-Tompkins QRS peak detection algorithm, FIR filters are utilized. Raw ECG signal can be fed to a Moving window filter, which helps filter out noise and get the signal of interest. These FIR filters involve the use of multipliers and adders, which take in several input sample and output a single sample. This paper replaces accurate adders in such filters with 10 16-bit signed approximate adders (power ve error parameters) from the EvoApprox library. Functional validation is done in MATLAB with Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of Moving Window Integration; and Mean Square Error (MSE) of thresholds as error metrics. MIT-BIH Arrythmia database is used as the raw ECG input. In hardware evaluation, a 100-point FIR filter is implemented with a single Multiply-accumulate unit where the exact adder is replaced with selected approximate adder. RTL model is synthesized with 45nm NandGate Open Cell library in Synopsys design compiler. Area, Average power, and Worst-case delay are measured. On average the presented methodology provides an area-saving of 19.71% and power-saving of 19.27%.
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...
Subhajit Sahu
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1. Old vs New tax regime [Form26AS] 2. Quarterly Tax Deducted at Source (TDS) [Form16] 3. Quarterly Tax Deducted at Source (other income) [Form 15G/H] 4. Quarterly Advance Tax (extra income) [Challan ITNS 280] Extras: - Public Provident Fund scheme (PPF) - National Pension Scheme (NPS) References: - https://www.incometax.gov.in/iec/foportal - https://finshots.in/archive/finshots-money-resolution-4-axe-your-tax/
Income Tax Calender 2021 (ITD) : NOTES
Income Tax Calender 2021 (ITD) : NOTES
Subhajit Sahu
Â
Groceries, nutrition kits, vegetables, women sanitation kits, heavy rain relief, awareness sessions, and a lot of help to people in need, including children.
Youngistaan Foundation: Annual Report 2020-21 : NOTES
Youngistaan Foundation: Annual Report 2020-21 : NOTES
Subhajit Sahu
Â
5 Activites were performed by members of Youngistaan Foundation in order to help increase voting turnup for Telangana 2018 elections, among the youth of Hyderabad.
Youngistaan: Voting awarness-campaign : NOTES
Youngistaan: Voting awarness-campaign : NOTES
Subhajit Sahu
Â
Highlighted notes on: Cost Efficient PageRank Computation using GPU This paper discusses the use of Aitken extrapolated Power method for PageRank computation. However, the results are unclear whether the performance improvement is due to GPU implementation, or due to use of Aitken extrapolation. The paper mentions a good performance improvement for damping factor values close to 1, and very low tolerance values which are usually not used for PageRank computation. It needs to be cross-checked to see if Aitken extrapolation provides and reduction of iterations on CPU only (as the same effect would be observed on the GPU, only timings change).
Cost Efficient PageRank Computation using GPU : NOTES
Cost Efficient PageRank Computation using GPU : NOTES
Subhajit Sahu
Â
This is my report on: Rank adjustment strategies for Dynamic PageRank (v1). While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli. Abstract â To avoid calculating ranks of vertices in a dynamic graph from scratch for every snapshot, the ones computed in the previous snapshot of the graph can be used, with adjustment. Four different rank adjustment strategies for dynamic PageRank are studied here. These include zero-fill, 1/N-fill, scaled zero-fill, and scaled 1/N-fill. Results indicate that the scaled 1/N-fill strategy requires the least number of iterations, on average. As long as the graph has no affected dead ends (including dead ends in the previous snapshot), unaffected vertices can be skipped with this adjustment strategy. Index terms â PageRank algorithm, Dynamic graph, Rank adjustment, Initial ranks.
Rank adjustment strategies for Dynamic PageRank : REPORT
Rank adjustment strategies for Dynamic PageRank : REPORT
Subhajit Sahu
Â
Highlighted notes on: Variadic CRTP by Steve Dewhurst (28 March 2017) Thus is a very good method of using CRTP for separation of concerns. The CuriouslyâRecurring Template Pattern (CRTP) is a pleasant and common C++ coding idiom. Basically itâs a way for a class to inherit a selfâcustomized capability.
Variadic CRTP : NOTES
Variadic CRTP : NOTES
Subhajit Sahu
Â
Highlighted notes on: Proceedings Scholar Metrics: H Index of proceedings on Computer Science, Electrical & Electronic Engineering, and Communications according to Google Scholar Metrics (2009-2013) The objective of this report is to present a list of proceedings (conferences, workshops, symposia, meetings) in the areas of Computer Science, Electrical & Electronic Engineering, and Communications covered by Google Scholar Metrics and ranked according to their hindex. Google Scholar Metrics only displays publications that have published at least 100 papers and have received at least one citation in the last five years (2009-2013). The searches were conducted between the 15th and 22nd of December, 2014. A total of 1208 proceedings have been identified.
Proceedings Scholar Metrics : NOTES
Proceedings Scholar Metrics : NOTES
Subhajit Sahu
Â
Highlighted notes on: Top CSE conferences list (IIIT Hyderabad) ICDE: International Conference on Data Engineering WWW: International World Wide Web Conference VLDB: International Conference on Very Large Databases ICDM: International Conference on Data Mining ICDE: International Conference on Data Engineering WSDM: International Conference on Web Search and Data Mining POPL: ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages PLDI: ACM SIGPLAN Conference on Programming Language Design and Implementation OOPSLA: Conference on Object Oriented Programming Systems Languages and Applications PODC: ACM Symposium on Principles of Distributed Computing SODA: ACM/SIAM Symposium on Discrete Algorithms ASPLOS: Architectural Support for Programming Languages and Operating Systems SC: ACM/IEEE Supercomputing Conference IPDPS: International Parallel and Distributed Processing Symposium SIGMETRICS: ACM SIG on Computer and Communication Metrics and Performance ICSE: International Conference on Software Engineering FSE/ESEC: ACM SIGSOFT International Symposium on Foundations of Software Engineering
Top CSE conferences list (IIIT Hyderabad) : NOTES
Top CSE conferences list (IIIT Hyderabad) : NOTES
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Survey for extra-child-process package : NOTES
Survey for extra-child-process package : NOTES
Â
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Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...
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Fast Incremental Community Detection on Dynamic Graphs : NOTES
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Can you ďŹx farming by going back 8000 years : NOTES
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HITS algorithm : NOTES
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Basic Computer Architecture and the Case for GPUs : NOTES
Â
Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES
Dynamic Batch Parallel Algorithms for Updating Pagerank : SLIDES
Â
Are Satellites Covered in Gold Foil : NOTES
Are Satellites Covered in Gold Foil : NOTES
Â
Taxation for Traders < Markets and Taxation : NOTES
Taxation for Traders < Markets and Taxation : NOTES
Â
A Generalization of the PageRank Algorithm : NOTES
A Generalization of the PageRank Algorithm : NOTES
Â
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Comp...
Â
Income Tax Calender 2021 (ITD) : NOTES
Income Tax Calender 2021 (ITD) : NOTES
Â
Youngistaan Foundation: Annual Report 2020-21 : NOTES
Youngistaan Foundation: Annual Report 2020-21 : NOTES
Â
Youngistaan: Voting awarness-campaign : NOTES
Youngistaan: Voting awarness-campaign : NOTES
Â
Cost Efficient PageRank Computation using GPU : NOTES
Cost Efficient PageRank Computation using GPU : NOTES
Â
Rank adjustment strategies for Dynamic PageRank : REPORT
Rank adjustment strategies for Dynamic PageRank : REPORT
Â
Variadic CRTP : NOTES
Variadic CRTP : NOTES
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Proceedings Scholar Metrics : NOTES
Proceedings Scholar Metrics : NOTES
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Top CSE conferences list (IIIT Hyderabad) : NOTES
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Microsoft 365 Copilot is available to Enterprise customers and smaller organizations from January 16, 2024. It goes for the price set at $30 per user/month across all purchasing channels and customer types. Microsoft 365 Copilot gives users experiences allowing for the composition and replying of emails, chats, documents, contacts, and meeting minutes.âŻâŻâŻ Microsoft 365 Copilot is available across all sales channels including Enterprise Agreement and Cloud Solution ProviderâŻ(CSP).⯠Also, for commercial customers with: ⯠⢠Microsoft 365 E3⯠⢠Microsoft 365 E5⯠⢠Office 365 E3⯠⢠Office 365 E5⯠⢠Microsoft 365 Business Standard⯠⢠Microsoft 365 Business Premium⯠They will be able to purchase Copilot for Microsoft 365 add-on SKU across all Microsoft sales channels. See below the full list of the Microsoft sales channels for Copilot:⯠⢠Enterprise Agreement⯠⢠Enterprise Agreement Subscription⯠⢠Cloud Solution Provider (CSP)⯠⢠Microsoft Customer Agreement â EnterpriseâŻ(MCA-E)⯠⢠Direct from Microsoft⯠⢠Education customers through Enrollment for Education Solutions⯠You can reach out to our team of Microsoft licensing experts concerning any on-premises or cloud related questions seeking for clarity. Send us an emailâŚinfo@q-advise.com
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