Distance Vector Multicast Routing Protocol : Notes
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Highlighted notes while studying Advanced Computer Networks: Distance Vector Multicast Routing Protocol Source: Wikipedia The Distance Vector Multicast Routing Protocol (DVMRP), defined in RFC 1075, is a routing protocol used to share information between routers to facilitate the transportation of IP multicast packets among networks. It formed the basis of the Internet's historic multicast backbone, Mbone. Wikipedia is a free online encyclopedia, created and edited by volunteers around the world and hosted by the Wikimedia Foundation.
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![10/25/2020 Distance Vector Multicast Routing Protocol - Wikipedia
https://en.wikipedia.org/wiki/Distance_Vector_Multicast_Routing_Protocol 1/2
Distance Vector Multicast Routing
Protocol
The Distance Vector Multicast Routing Protocol (DVMRP), defined in RFC 1075, is a routing
protocol used to share information between routers to facilitate the transportation of IP multicast
packets among networks. It formed the basis of the Internet's historic multicast backbone, Mbone.
Operation
Criticisms
See also
References
External links
The protocol is based on the Routing Information Protocol (RIP).[1] The router generates a routing
table with the multicast group of which it has knowledge with corresponding distances (i.e. number of
devices/routers between the router and the destination). When a multicast packet is received by a
router, it is forwarded by the router's interfaces specified in the routing table.
DVMRP operates via a reverse path flooding technique, sending a copy of a received packet
(specifically IGMP messages for exchanging routing information with other routers) out through each
interface except the one at which the packet arrived. If a router (i.e. a LAN which it borders) does not
wish to be part of a particular multicast group, it sends a "prune message" along the source path of
the multicast.
Being a naïve distance-vector protocol, DVMRP has difficulties with network scaling in some
topologies,[2] primarily due to the periodic reflooding necessary to detect new hosts. This was more
prevalent in early versions of the protocol, prior to the implementation of pruning.[3] DVMRP's flat
unicast routing mechanism, which is used to determine the source interface of a data stream, also
affects its ability to scale.
IEEE 802.1aq - Shortest Path Bridging (SPB)
IP Multicast
Protocol Independent Multicast
Router Information Protocol
Contents
Operation
Criticisms
See also
References](https://image.slidesharecdn.com/distancevectormulticastroutingprotocol-wikipedia-201025173626/85/Distance-Vector-Multicast-Routing-Protocol-Notes-1-320.jpg)
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range therefore multiple
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Protocols. In this paper we also compare different routing proto
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parameters
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-
ov
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Survey comparison estimation of various routing protocols in mobile ad hoc ne...
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range therefore multiple
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Multicasting allows data to be sent from one source to multiple receivers simultaneously. It provides an efficient way to disseminate information to many recipients. The document discusses IP multicast addressing, the IGMP protocol for joining and leaving multicast groups, multicast routing protocols like DVMRP and PIM, and methods for constructing multicast distribution trees like source-based and shared trees. Multicasting is important for applications like streaming media and teleconferencing that require one-to-many or many-to-many communication.
Network Surveillance Based Data Transference in Cognitive Radio Network with ...
The document compares different wireless routing protocols to find the most energy efficient for creating a cognitive radio network model with attacker nodes. It first describes cognitive radio networks and their ability to dynamically access unused radio spectrum. It then summarizes the characteristics of reactive, proactive, and hybrid routing protocols. Reactive protocols determine routes on demand through flooding, while proactive protocols constantly update routing tables. The document analyzes the ad hoc on-demand distance vector (AODV) and dynamic source routing (DSR) reactive protocols as well as the destination sequenced distance vector (DSDV) and optimized link state (OLSR) proactive protocols. It aims to compare these protocols and determine the most energy efficient for the cognitive radio network model.
Network Surveillance Based Data Transference in Cognitive Radio Network with ...
This document compares different wireless routing protocols to find the most energy efficient for creating a cognitive radio network model with attacker nodes. It analyzes reactive, proactive, and hybrid routing protocols including AODV, DSR, DSDV, OLSR, and a hybrid protocol. Simulation results show the hybrid protocol consumes the least energy compared to other protocols, making it well-suited for an energy efficient cognitive radio network model.
Broadcasting routing protocols in vanet
This document discusses various routing protocols for vehicular ad hoc networks (VANETs). It describes five main categories of routing protocols for VANETs: topology based, position based, geo-cast, broadcast, and cluster based. It focuses on broadcast routing protocols, giving examples like BROADCOMM, UMB, V-TRADE, and DV-CAST. Broadcast protocols are important for disseminating traffic, weather, and emergency information between vehicles. The document provides an overview of the advantages and disadvantages of different routing approaches for supporting intelligent transportation systems.
11.broadcasting routing protocols in vanet
This document discusses various routing protocols for vehicular ad hoc networks (VANETs). It begins by introducing VANETs and their applications. It then categorizes and describes five main types of routing protocols for VANETs: topology based, position based, geo cast, broadcast, and cluster based. It focuses on describing different types of broadcast routing protocols, outlining nine specific protocols (such as BROADCOMM, UMB, V-TRADE, DV-CAST). The document concludes that designing an efficient routing protocol for all VANET applications is challenging and that surveying different protocols is important to develop new proposals.
DVRM
Distance-vector routing protocols determine the best route based on the number of hops a packet has to pass. Routers exchange information with each other periodically to determine the best route across the network. Each router maintains a distance vector table containing the distance to all possible destinations, which are calculated using information from neighbors' tables.
A Survey on Network Layer Multicast Routing Protocols for Mobile Ad Hoc Netw...
This document summarizes a survey on network layer multicast routing protocols for mobile ad hoc networks. It begins with an introduction to multicast routing in MANETs and their advantages over unicast. It then provides an overview of the architecture for multicast routing protocols, which consists of three layers: the MAC, routing, and application layers. The document classifies and discusses issues with designing multicast protocols for MANETs. It also categorizes existing multicast routing protocols based on the structure they construct (tree or mesh based), how sessions are initialized (source or receiver initiated), and how topology is maintained (proactively or reactively). Several example protocols are described briefly, including MAODV, AMRIS, and
A Survey on Network Layer Multicast Routing Protocols for Mobile Ad Hoc Netw...
This document summarizes a survey on network layer multicast routing protocols for mobile ad hoc networks. It begins with an introduction to multicast routing in MANETs and their advantages over unicast. It then provides an overview of the architecture for multicast routing protocols, which consists of three layers: the MAC, routing, and application layers. The document classifies and discusses issues with designing multicast protocols for MANETs. It surveys several representative tree-based and mesh-based multicast routing protocols, describing their operation, advantages, and disadvantages at a high level. Key protocols discussed include MAODV, AMRIS, ODMRP, and CAMP.
Arun project-Final
This document describes a student project to implement the OSPF routing protocol on routers using the Packet Tracer simulator. It includes an introduction to routing and OSPF, as well as chapters covering the OSPF process, router types, network architecture, results, advantages/disadvantages, and references. The project was completed by three students for their Bachelor of Technology degree and submitted to their department for acceptance.
ROUTING
1. Enable the OSPF routing process with a process ID. This groups related OSPF commands.
2. Configure OSPF areas. Areas organize groups of networks/links in the OSPF domain.
3. Associate networks with their corresponding areas using the network command. This establishes OSPF neighbor relationships between routers on the network.
Research Inventy : International Journal of Engineering and Science
The document summarizes a study that evaluated the performance of three mobile ad hoc network (MANET) routing protocols: AODV, DSDV, and DSR. The protocols were simulated using the NS-2 network simulator across networks of 30 to 70 nodes. Key performance metrics analyzed include packet delivery fraction, average end-to-end delay, normalized routing load, and packet loss. The results found that AODV performed best in terms of packet delivery fraction and shortest end-to-end delay, while DSDV had the lowest normalized routing load and DSR had the lowest packet loss. Overall, the document compares the performance of these three MANET routing protocols under different conditions using simulation results.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
This document summarizes a research paper that evaluates the performance of two routing protocols (AODV and DSDV) under different traffic patterns (TCP and CBR) in a mobile ad hoc network (MANET) simulation. The paper describes MANET characteristics and challenges for routing. It provides an overview of reactive (AODV), proactive (DSDV), and hybrid routing protocols. It also defines TCP and CBR traffic patterns. The research aims to analyze and compare the packet delivery ratio and end-to-end delay of AODV and DSDV under different traffic loads using the NS-2 simulator. Preliminary results show that reactive protocols perform better in terms of these metrics.
Jb2516071610
This document evaluates the performance of the AODV and DSR routing protocols for Variable Bit Rate (VBR) multimedia traffic in mobile ad hoc networks. It conducts simulations in NS-2 to analyze four performance metrics: packet received, throughput, routing overhead, and network load. The results show that AODV outperforms DSR for packet received, throughput, and routing overhead. DSR has higher network load than AODV. Therefore, the document concludes that AODV is better suited than DSR for VBR multimedia transmission in mobile ad hoc networks.
Jb2516071610
This document evaluates the performance of the AODV and DSR routing protocols for Variable Bit Rate (VBR) multimedia traffic in mobile ad hoc networks. It conducts simulations in NS-2 to analyze four performance metrics: packet received, throughput, routing overhead, and network load. The results show that AODV outperforms DSR for packet received, throughput, and routing overhead. DSR has higher network load than AODV. Therefore, the document concludes that AODV is better suited than DSR for VBR multimedia transmission in mobile ad hoc networks.
Az26337342
The document analyzes the performance of two routing protocols for mobile ad hoc networks (MANETs) - the Wireless Routing Protocol (WRP) and the Ad Hoc On-Demand Distance Vector (AODV) protocol. It describes the simulation setup used to evaluate the protocols under different scenarios varying offered load, pause time, and node speed. The results show that AODV outperformed WRP in terms of packet delivery ratio, throughput, and average end-to-end delay in most scenarios, demonstrating that AODV is generally better suited for MANETs compared to WRP. The same simulation framework could be used to evaluate other routing protocols.
A Survey on Cross Layer Routing Protocol with Quality of Service
Wireless is playing the wide role in today’s industrial application. Central idea of this paper is to enhance quality of service (QoS) for multimedia transmission over ad-hoc network. This paper describes the operational of different QoS routing protocols, their properties and various parameters advantages and disadvantages. Also describes the use of QoS in Cross layer routing protocol. Finally, it concludes by study of all these cross layer QoS routing protocols.
Routing protocols in Ad-hoc Networks- A Simulation Study
This document summarizes a study that simulated and compared several routing protocols for ad hoc networks using the Network Simulator 2 (NS-2). It began by describing ad hoc networks and reviewing related work comparing routing protocols. It then provided an overview of desirable routing protocol properties and described several prominent protocols in detail: DSDV, AODV, DSR, ZRP, TORA, IMEP, and CBRP. The document outlined the NS-2 simulator and the mobility extension used. It concluded by detailing the simulation methodology and modifications made to the AODV and DSR implementations in NS-2 to perform the comparative analysis.
Similar to Distance Vector Multicast Routing Protocol : Notes (20)
Survey comparison estimation of various routing protocols in mobile ad hoc ne...
Survey comparison estimation of various routing protocols in mobile ad hoc ne...
Survey comparison estimation of various routing protocols in mobile ad hoc ne...
Survey comparison estimation of various routing protocols in mobile ad hoc ne...
Basicsofmulticastinganditsimplementationonethernetnetworks
Basicsofmulticastinganditsimplementationonethernetnetworks
Basics of multicasting and its implementation on ethernet networks
Basics of multicasting and its implementation on ethernet networks
Network Surveillance Based Data Transference in Cognitive Radio Network with ...
Network Surveillance Based Data Transference in Cognitive Radio Network with ...
Network Surveillance Based Data Transference in Cognitive Radio Network with ...
Network Surveillance Based Data Transference in Cognitive Radio Network with ...
A Survey on Network Layer Multicast Routing Protocols for Mobile Ad Hoc Netw...
A Survey on Network Layer Multicast Routing Protocols for Mobile Ad Hoc Netw...
A Survey on Network Layer Multicast Routing Protocols for Mobile Ad Hoc Netw...
A Survey on Network Layer Multicast Routing Protocols for Mobile Ad Hoc Netw...
Research Inventy : International Journal of Engineering and Science
Research Inventy : International Journal of Engineering and Science
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
IJCER (www.ijceronline.com) International Journal of computational Engineerin...
A Survey on Cross Layer Routing Protocol with Quality of Service
A Survey on Cross Layer Routing Protocol with Quality of Service
Routing protocols in Ad-hoc Networks- A Simulation Study
Routing protocols in Ad-hoc Networks- A Simulation Study
More from Subhajit Sahu
About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...
TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting Bitset for graph : SHORT REPORT / NOTES
Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Experiments with Primitive operations : SHORT REPORT / NOTES
This includes:
- Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
- Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
- Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
- Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
PageRank Experiments : SHORT REPORT / NOTES
This includes:
- Adjusting data types for rank vector
- Adjusting Pagerank parameters
- Adjusting Sequential approach
- Adjusting OpenMP approach
- Comparing sequential approach
- Adjusting Monolithic (Sequential) optimizations (from STICD)
- Adjusting Levelwise (STICD) approach
- Comparing Levelwise (STICD) approach
- Adjusting ranks for dynamic graphs
- Adjusting Levelwise (STICD) dynamic approach
- Comparing dynamic approach with static
- Adjusting Monolithic CUDA approach
- Adjusting Monolithic CUDA optimizations (from STICD)
- Adjusting Levelwise (STICD) CUDA approach
- Comparing Levelwise (STICD) CUDA approach
- Comparing dynamic CUDA approach with static
- Comparing dynamic optimized CUDA approach with static
Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting OpenMP PageRank : SHORT REPORT / NOTES
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...
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.
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
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.
Shared memory Parallelism (NOTES)
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].
A Dynamic Algorithm for Local Community Detection in Graphs : NOTES
**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.
Scalable Static and Dynamic Community Detection Using Grappolo : NOTES
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_.
Application Areas of Community Detection: A Review : NOTES
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)
Community Detection on the GPU : NOTES
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
Survey for extra-child-process package : NOTES
Useful additions to inbuilt child_process module.
📦 Node.js, 📜 Files, 📰 Docs.
Please see attached PDF for literature survey.
https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER
This paper presents two algorithms for efficiently computing PageRank on dynamically updating graphs in a batched manner: DynamicLevelwisePR and DynamicMonolithicPR. DynamicLevelwisePR processes vertices level-by-level based on strongly connected components and avoids recomputing converged vertices on the CPU. DynamicMonolithicPR uses a full power iteration approach on the GPU that partitions vertices by in-degree and skips unaffected vertices. Evaluation on real-world graphs shows the batched algorithms provide speedups of up to 4000x over single-edge updates and outperform other state-of-the-art dynamic PageRank algorithms.
Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...
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
Fast Incremental Community Detection on Dynamic Graphs : NOTES
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
More from Subhajit Sahu (20)
About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...
About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Adjusting primitives for graph : SHORT REPORT / NOTES
Adjusting primitives for graph : SHORT REPORT / NOTES
Experiments with Primitive operations : SHORT REPORT / NOTES
Experiments with Primitive operations : SHORT REPORT / NOTES
Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...
Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...
word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES
A Dynamic Algorithm for Local Community Detection in Graphs : NOTES
A Dynamic Algorithm for Local Community Detection in Graphs : NOTES
Scalable Static and Dynamic Community Detection Using Grappolo : NOTES
Scalable Static and Dynamic Community Detection Using Grappolo : NOTES
Application Areas of Community Detection: A Review : NOTES
Application Areas of Community Detection: A Review : NOTES
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER
Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER
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...
Fast Incremental Community Detection on Dynamic Graphs : NOTES
Fast Incremental Community Detection on Dynamic Graphs : NOTES
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办理新西兰奥克兰大学毕业证学位证书范本原版一模一样
原版一模一样【微信：741003700 】【新西兰奥克兰大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
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7:个人信誉贷款加10分
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怎么办理(umiami毕业证书)美国迈阿密大学毕业证文凭证书实拍图原版一模一样
原版定制【微信:bwp0011】《(umiami毕业证书)美国迈阿密大学毕业证文凭证书》【微信:bwp0011】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信bwp0011】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信bwp0011】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
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快速办理(Vic毕业证书)惠灵顿维多利亚大学毕业证完成信一模一样
学校原件一模一样【微信：741003700 】《(Vic毕业证书)惠灵顿维多利亚大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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一比一原版(USYD毕业证)悉尼大学毕业证如何办理
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如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
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6、企事业单位必须要求办理的；
面向美国乔治城大学毕业留学生提供以下服务:
【★悉尼大学悉尼大学毕业证offer毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
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【★真实留信认证，留信网入库存档，可查悉尼大学悉尼大学毕业证offer】
我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助悉尼大学同学朋友
你做代理，可以拯救悉尼大学失足青年
你做代理，可以挽救悉尼大学一个个人才
你做代理，你将是别人人生悉尼大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会过暑假的小学生快乐的日子总是过得飞快山娃尚未完全认清那几位小朋友时他们却一个接一个地回家了山娃这时才恍然发现二个月的暑假已转到了尽头他的城市生活也将划上一个不很圆满的句号了值得庆幸的是山娃早记下了他们的学校和联系方式说也奇怪在山娃离城的头一天父亲居然请假陪山娃耍了一天那一天父亲陪着山娃辗转长隆水上乐园疯了一整天水上漂流高空冲浪看大马戏大凡里面有的父亲都带着他去疯一把山娃算了算这一次足足花了老爸元很
一比一原版新西兰林肯大学毕业证(Lincoln毕业证书)学历如何办理
原版办【微信号:95270640】【新西兰林肯大学毕业证(Lincoln毕业证书)】【微信号:95270640】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
HijackLoader Evolution: Interactive Process Hollowing
CrowdStrike researchers have identified a HijackLoader (aka IDAT Loader) sample that employs sophisticated evasion techniques to enhance the complexity of the threat. HijackLoader, an increasingly popular tool among adversaries for deploying additional payloads and tooling, continues to evolve as its developers experiment and enhance its capabilities.
In their analysis of a recent HijackLoader sample, CrowdStrike researchers discovered new techniques designed to increase the defense evasion capabilities of the loader. The malware developer used a standard process hollowing technique coupled with an additional trigger that was activated by the parent process writing to a pipe. This new approach, called "Interactive Process Hollowing", has the potential to make defense evasion stealthier.
快速办理(新加坡SMU毕业证书)新加坡管理大学毕业证文凭证书一模一样
学校原件一模一样【微信：741003700 】《(新加坡SMU毕业证书)新加坡管理大学毕业证文凭证书》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
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Distance Vector Multicast Routing Protocol : Notes
- 1. 10/25/2020 Distance Vector Multicast Routing Protocol - Wikipedia https://en.wikipedia.org/wiki/Distance_Vector_Multicast_Routing_Protocol 1/2 Distance Vector Multicast Routing Protocol The Distance Vector Multicast Routing Protocol (DVMRP), defined in RFC 1075, is a routing protocol used to share information between routers to facilitate the transportation of IP multicast packets among networks. It formed the basis of the Internet's historic multicast backbone, Mbone. Operation Criticisms See also References External links The protocol is based on the Routing Information Protocol (RIP).[1] The router generates a routing table with the multicast group of which it has knowledge with corresponding distances (i.e. number of devices/routers between the router and the destination). When a multicast packet is received by a router, it is forwarded by the router's interfaces specified in the routing table. DVMRP operates via a reverse path flooding technique, sending a copy of a received packet (specifically IGMP messages for exchanging routing information with other routers) out through each interface except the one at which the packet arrived. If a router (i.e. a LAN which it borders) does not wish to be part of a particular multicast group, it sends a "prune message" along the source path of the multicast. Being a naïve distance-vector protocol, DVMRP has difficulties with network scaling in some topologies,[2] primarily due to the periodic reflooding necessary to detect new hosts. This was more prevalent in early versions of the protocol, prior to the implementation of pruning.[3] DVMRP's flat unicast routing mechanism, which is used to determine the source interface of a data stream, also affects its ability to scale. IEEE 802.1aq - Shortest Path Bridging (SPB) IP Multicast Protocol Independent Multicast Router Information Protocol Contents Operation Criticisms See also References
- 2. 10/25/2020 Distance Vector Multicast Routing Protocol - Wikipedia https://en.wikipedia.org/wiki/Distance_Vector_Multicast_Routing_Protocol 2/2 1. DVMRP: Distance Vector Multicast Routing Protocol (http://www.javvin.com/protocolDVMRP.html) Archived (https://web.archive.org/web/20090622072943/http://www.javvin.com/protocolDVMRP.ht ml) June 22, 2009, at the Wayback Machine 2. What is DVMRP? (http://www.savetz.com/mbone/apc_13.html) 3. Multicast Routing May 5, 1995 (http://www.cisco.com/en/US/tech/tk828/technologies_white_pape r09186a00800a43ee.shtml) RFC 1075 - Distance Vector Multicast Routing Protocol Proposal for Distance Vector Multicast Routing Protocol version 3 (http://tools.ietf.org/html/draft-ie tf-idmr-dvmrp-v3-11) Retrieved from "https://en.wikipedia.org/w/index.php? title=Distance_Vector_Multicast_Routing_Protocol&oldid=846595601" This page was last edited on 19 June 2018, at 18:57 (UTC). Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. By using this site, you agree to the Terms of Use and Privacy Policy. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization. External links