Right Relevance (RR) provides curated information and intelligence on ~50 thousand topics. This includes:
- Topic relationships including related topics & semantic information like synonyms.
- Topical influencers (~2.5M) with score and rank.
- Topical content and information in the form of articles, videos and conversations.
Additionally, Right Relevance provides an Insights offering that combines the above Topics and Influencers information with real time conversations to provide actionable intelligence with visualizations to enable decision making. The Insights service is applicable to emerging events like elections, conferences, product launches, breaking news developments, outbreaks like Ebola etc.
This report is a summary of graph analysis of engagements and conversations including retweets, mentions and replies of tweets from Jan and Feb 2017 on terms related to ‘Software Defined Networking’ (SDN) and ‘Network Functions Virtualization’ (NFV) along with ‘Cloud Computing’, Openstack, Openflow, Devops & OpenContrail.
Data & Duration
The report leverages tweets sampled from Jan 1st 2017 to February 28th 2017 and along with Right Relevance topics, topical communities’ and articles data form the basis for the analysis.
The phrases used for gathering tweets are: “#sdn”, “software defined networking”, “softwaredefinednetworking”, “#nfv”, “network functions virtualization”, “networkfunctionsvirtualization”, “cloud computing”, “cloudcomputing”, “openstack”, “openflow”, “devops”, “opencontrail”
The focus of this report is exclusively on SDV & NFV with some coverage of ‘openflow’ and ‘openstack’. Topics related to ‘cloud computing’ and ‘devops’ were added to the mix to get a broader perspective. Consequently, as we’ll see, large parts of the graphs and several communities are dominated by cloud computing, devops, AWS, MSFT/Azure etc. We’ll ignore those for the purpose of this summary report.
Most of the summary report is extracted from the analysis collateral in the form of:
- Tableau Online Dashboard
- Gephi Communities Graph Visual: Extracts are shown below.
Community detection graph algorithms like Walktrap and InfoMap are used to identify communities (as sub-graphs) in our engagements graph built using Neo4j & R. Graph visualizations are done via Gephi.
The communities’ graph (Fig 1) visualizes the broader area involving ‘cloud computing’ and ‘devops’. It clearly shows SDN/NFV communities are a tightly knit subgroup within the broader cloud computing space and co-located with ‘openstack’ related community.
We’ll dive deeper into the sub-communities and the leading users driving each sub-community in the Flocks section.
Top Conversational Themes
Latent Dirichlet allocation (LDA) based text analysis of the tweets is used for identifying high value trending terms. These along with hashtags and Right Relevance topics form the basis for identifying top conversation themes during the analysis timeframe.
Looking at the trending terms, hashtags and RR topics (Fig 2), we can make out the following top themes driving conversations during Jan & Feb 2017:
- Cloud computing, linux, openstack, data center, virtualization etc. closely related group of topics
- Big data and IoT
- Networking and SDN
- AWS, Azure, Agile programming, open source software, PaaS, SaaS etc. many related topics are in the mix
The communities and conversations related to the top themes will be outlined and discussed in greater details below as we delve deeper.
Topical Influence: Tribes
Measuring influence is not deterministic. It’s a subjective task with numerous different methodologies and is generally ephemeral in nature. Right Relevance platform measures users/accounts influence in 2 distinct ways: topical & engagement-based.
Right Relevance algorithmically mines topics, content and social media at scale to produce a measure of influence per topic termed as ‘topical influencers’ or Tribes. Unstructured text, network connections, social signals along with semantic data, ML, NLP are leveraged to produce two sets of information; a set of ‘structured topics’ (~50K) with semantic information and; a connected graph of scored ranked influencers for each of these structured topics we call ‘topical influencers’ or Tribes.
For e.g. ‘software defined networking’ is a structured topic in the Right Relevance platform with the following metadata (Fig 3) returned by the RR Topics Metadata API of the RR API offering.
The top related topics include data center, network functions virtualization, virtualization, cloud computing among others.
Right Relevance also identified over 1K scored ranked set of SDN influencers on Right Relevance. The top 5 are listed in Fig 4 below.
Fig 5 provides a connected graph view of the top 30 influencers for ‘SDN’ showing how ‘Juniper Networks’ is connected to other influencers within the top 30.
Engagements-based Influence: Flocks
Right Relevance ‘engagement influence’ is calculated by measuring the quality and quantity of engagements (RTs, mentions, replies), reach of tweets etc. within the context of a subject (event, trend etc.). The communities formed via this methodology are termed as ‘Flocks’. Flocks are people engaging in conversations around events esp. in context of a specific subject, which is ‘software defined networking’ terms in this case. This “flocking” can lead to building of temporal communities with local influence that can lead to virality not obvious by the standalone influence of the individuals or without the context of the event.
We apply several methods including PageRank and Betweeness centrality to measure Flock influence. The meaning of rankings within this methodology are documented at Twitter Conversation Performance Measures.
The Flock analysis details will be focused on the main SDN/NFV based flock (community) found by our analysis, named after the highest PageRank account, ‘OpenDaylightSDN’.
Looking at the hashtags and RR topics (Fig 6), OpendaylightSDN seems like the primary SDN & NFV related community aka flock.
Rank based Influence Measure
The first two lists (Fig 7) are of the top 30 accounts by PageRank & Overall measures. Overall rank is a normalized rank to reduce the skew towards users with large numbers of followers or a single tweet having a large number of engagements/RTs (often referred to as becoming ‘viral’).
Both measures bring up @OpenDaylightSDN, @inocybetech, @sdxcentral, @WindRiver, @sdxtech, @JimStLeger, @Cloud_SDN, @openflow, @opnfv as the main flock influencers for SDN.
In this case, the top overall measure, in spite to the normalized nature, didn’t bring up many new users to the top. @OpenContrail, @SearchSDN and @SDN_RR are 3 accounts that were moved up appreciably by the top overall method.
The results above lead to other measures becoming important to measure influence as discussed below.
Betweeness Centrality based Influence Measure: Connectors
The top ‘connectors’ list is based on Betweeness centrality, which is a measure of the degree to which a node forms a bridge or critical link between all other users. We use this as a measure of influence wrt value in being information and/or communication hubs.
The first thing of notice is that most of the top accounts by PageRank and Overall measures are missing. This measure brings up potentially hard to discover users like @samcmgl, @olivierfroggy, @HealyJohnV, @5GPPP_SELFNET, @OpenSourceMANO, @SandraLRivera, @emaganap, @asingla77 among others.
The value of this measure lies in that it bubbles up accounts with potentially real influence in terms of news and information dissemination on this subject.
OpenDaylightSDN: Reach Vs Rank
Another view to understand influence is to plot reach (audience/followers) against a normalized measure we call rank (authority). This is another great way to dampen pure follower based metrics and to bring out users that hold more sway within the community itself.
The Reach (X-axis) Vs Rank (Y-axis) graph (Fig 9) throws up a couple of interesting things:
- High followers count accounts like SDxCentral and SDxTech have high reach and rank as expected, but even low follower accounts like ‘OpenDaylight Project’ and ‘Inocybe Technologies’ were able to gain comparable or higher rank during this timeframe.
- Many “connector” accounts ranked high via Betweeness Centrality measure have much higher rank disproportionate to their follower count and reach in many cases. This is probably because they are early movers and/or tightly integrated in this subject’s community.
OpenDaylightSDN: Communities Graph
The Gephi snapshot of the OpendaylightSDN flock (Fig 10) visualizes the community with most closely engaged accounts co-located.
OpenDaylightSDN: Top Tweets
The top tweets in Jan & Feb within the primary SDN community are:
Other Interesting FLOCKS aka Engaged Communities
Several viable flocks came up via graph analysis and visible in the Gephi graph. We’ll outline 2 of the most closely related one.
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The flock names below are named on the user/account with most PageRank in the flock.
‘CAInc’ flock forms a closely related community with a big overlap with SDN. This can be seen by related hashtags like #sdn along with related RR topics like ‘software defined networking’.
Fig 12 is a Gephi snapshot of the ‘CAInc flock sub-graph.
Fig 13 shows the top Tweet in the context of this flock.
‘OpenStack’ is another flock with primary focus on openstack, cloud computing and devops with a fairly big overlap with SDN & NFV. This can be seen by #nfv and #sdn being prominent hashtags (Fig 14). Related RR topics also have SDN among the top.
Fig 15 is a Gephi snapshot of the ‘OpenStack’ flock sub-graph.
A few quick observations of note are:
- Twitter is a source of vibrant conversations with extremely well defined communities, influential users and critical discussions for SDN/NFV.
- The SDN/NFV community is closely connected to Cloud Computing, Devops and other related terms with common set of influencers.
- SDN (NFV etc.) stands out on its own in the broader cloud space with identifiable influential actors and conversations.
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