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Dogecoin

Overview
Topology
Nodes

Networks
Dogecoin
Topology

Topology

Key metrics on network structure, geographic peer distribution, client diversity, and connection patterns across the Dogecoin network. All metrics in this section refer to reachable Dogecoin nodes over IPv4 and IPv6. Each address counts as one peer, so a node that listens on IPv4 and on IPv6 counts two times.

Geographic Distribution

Node IP address to country, city, and autonomous system (ASN) mapping, based on data sourced from https://ipregistry.co/.

Peers By City

This map shows the online peers per city in the last crawl of the latest day. The city and its coordinates come from the IP address mapping of ipregistry.co. Peers without a known city are not on the map. The text below the map shows how many peers are not on the map.

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Geolocation Over Time

DHT Server geolocation per agent version over time

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Countries Distribution (24h)

Snapshot of active agents geolocation within the last 24hrs

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Cities Over Time

Shows the number of online peers in the ten cities with the most peers over time. The other cities are in "Other". The city of a peer comes from the IP address mapping of ipregistry.co. Peers without a known city are not in this chart.

Online peers in the largest cities

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Autonomous Systems Over Time

Shows the number of online peers in the ten largest autonomous systems (ASNs) over time. The other ASNs are in "Other". The ASN of a peer comes from the IP address mapping of ipregistry.co. Peers without a known ASN are not in this chart.

Online peers in the largest autonomous systems (ASNs)

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ASN Concentration

Shows how concentrated the online peers are in autonomous systems (ASNs). Only peers with a known ASN count. Select a view:

Largest ASNs: the share of the peers in the largest ASN and in the ten largest ASNs.
ASNs for 50%: the smallest number of ASNs that together host half of the peers. A low number means that a few network operators host much of the network.
HHI: the Herfindahl-Hirschman index of the ASN shares, from 0 (spread over many ASNs) to 10,000 (all peers in one ASN).

How many autonomous systems host the online peers

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Agent Version Distributions

Network structure representation through client and agent distribution. The core agent type is Dogecoin Core, with the user agent Shibetoshi.

Client Diversity Trend

Shows the evolution of different user agent types (client software) participating in the network, over time.

Each agent type represents a different implementation and and indicates its relative prevalence within the network for the given reporting period. This chart helps track software diversity, adoption of new client versions, and overall network health, in terms of active nodes in the network.

The Unknown category represents nodes where the agent version could not be retrieved through the libp2p identify protocol. This situation can occur due to various reasons, such as the node terminating the connection before completing the full protocol negotiation.

Client participation over time

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Active Agents Distribution (24h)

Snapshot of active agents within the last 24hrs

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Agent Versions Trend

Agent version participation over time

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Agent Version Distribution (24h)

Snapshot of versions active per agent within the last 24hrs

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Transports & Infrastructure

Reachable peers per transport, the service flags that the nodes announce, and their infrastructure providers.

Supported Transports Over Time

Count of nodes support per transport protocol

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Supported Transports

Network snapshot in terms of supported transports

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Cloud Provider Dependency Over Time

Count of nodes deployed per cloud provider and per agent

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Supported Protocols Over Time

Number of peers supporting the top 40 protocols in the network.

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Errors

Connection and crawl errors breakdown per transport.

Dial Error Causality Distribution

The plot shows the percentage split of the cause of errors, when errors happen. For example, a 20% error due to, say, connection_refused does not mean that 20% of the time we attempted a connection to a node our attempt was refused. The percentage shown is out of the times that an error did occur, not the total number of attempts.

Percentage split of dial errors per error cause

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Crawl Errors Over Time

Monitors the percentage of crawl errors encountered during network topology discovery over time. Crawl errors occur when the monitoring system cannot successfully connect to or retrieve information from discovered peers. This metric helps identify network connectivity issues, configuration problems, or periods of increased network instability. Use the discovery method filter to analyze errors specific to DHT or GossipSub peer discovery.

Percentage split of crawl errors per error cause

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Measurement Tools

Our network monitoring employs advanced crawling and probing tools and techniques to gather comprehensive data about network health, topology, and performance.