Social Networks Elearning

Spoke builds enterprise applications that deliver insight from--and access to--human capital through intra- and extra-enterprise relationships Connecting People to Knowledge Through Relationships Andrew Halliday VP Business Development [email protected]

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Social Networks, and Social Network Analysis...Some Terms of Art •

Links are “arcs” between “nodes” in a “graph” – Links (arcs) between people can be: •

declared

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observed/recorded

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“Degrees of separation” measures the number of arcs separating nodes

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Most current web social networks are fashioned of declared arcs – Web interfaces for creating one’s node/profile, and workflow to collect arcs – Graph represents the “n degrees” of all inter-connected nodes – Users can typically navigate paths along arcs to friends-of-friends

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Social Network Analysis...two distinct kinds 1. simple mapping of declared social networks (arcs are undifferentiated) 2. analysis of digital messaging to establish social networks and create measures of strength of relationships (arcs are quantified)

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What if You could…

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Discover and measure all the private relationships held within a company and its partners, without data entry?

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Get everyone to participate because their personal relationship information is kept private, and under their control?

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Deliver cooperative insight and access from all company relationships with active participation and permission, opt-in?

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Bring any relationship the company, its partners, and affiliates have to bear on any business situation?

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Discover the knowledge resources the enterprise is connected to through relationships?

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Discovering organizational relationship capital Your workgroup network

Human Resources inside or outside the company

Northeast Division

West Division

“light up” all the channels of insight and access

© 2003 Spoke Software, Inc.

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Networked Users

+ those known by users

Message analysis extends Network Comprehension and Reach to all those known by networked users in email...with no data entry

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Reach in Social Networks, and Diversity •

Constraints on reach in “declared arc” networks –

Limited to those who join

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Limited to the scope of user’s input effort

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Limited to memorable/comfortable connections

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Automated discovery in electronic messaging enables broader reach, and comprehends many non-users

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Including those known to users creates a comprehensive map of social networks, and rich data, but this demands etiquette and privacy controls for users, and especially non-users

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System Messaging to/through users only

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Freedom from social pressure for introductions

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Inclusion of thousands of user correspondents requires differentiation among various categories/levels of relationship

The benefit is Diversity –

inclusion of both strong ties and “the strength of weak ties”

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Innovations and information value come from as-yet-unexplored relationship capital

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Extra-enterprise reach is essential to creating value in external and multi-company business alliances •

Many if not most relationships of value to the enterprise are outside the firewall

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Intra-enterprise discovery systems are limited to one degree beyond the firewall – Effective for intra-enterprise collaboration – Extra-enterprise collaboration depends on navigation beyond 1 degree

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Federation with broad public networks enables discovery of external relationships many degrees beyond the firewall

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“Private information self-determination” Maintaining privacy through individual control •

Your information is yours, for your eyes only...and my information is mine

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Never reveal contact information

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Never allow anyone to discover who-knows-whom

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Provide fine-grained control over movement of my data

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Provide articulate control over who can access/message me

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Allow permissioned disclosure of private information

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Design Principle: Relationships are owned by the individual, not the enterprise...any other premise results in sabotage

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Future Impact of Social Networks •

Search – by creating personalized context for results based on whom-you-know

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Commerce – by facilitating relevant influencer effects on Tipping Points in societies – personally relevant peer reviews based on network proximity

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Communities – by supporting flexible, personally-defined networks and subnetworks

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Media – by promoting and coordinating content choices within cohorts, creating communal experience and keeping you au courant with your cohort

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Online Social Networks will have an impact on internetworking...these are some possible developments 1. The emergence of extensive online registries with personal profiles 2. New model of relevance for peoplesearch...”Who matters most are those most connected, and/or those most connected to you” 3. Profiles derived from social cohorts will be used for content routing, search personalization, and alignment into communities-of-interest 4. The emergence of trusted messaging networks based on relationship managers can replace the polluted general emailbox 5. Social networks will be used for knowledge collection, collaboration, and dissemination, with higher personal relevance based on society

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Personal Context and Productivity: Managing the volume problem in the digital environment

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Person-centric knowledge assets are auto-generated in Social Network systems Visualization of Personal Network

• Privacy maintained • Individual or organizational • Handles massive data organization

Building Sharable Dossiers

• Automated data collection on persons • Plus manual collection and annotation • Research asset accumulates

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1. Online registry of professional and personal profiles •

Social Networks are providing a motive for personal self-profiling

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Publish Profile with multi-tiered access permissions-management

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based on relationship levels and roles

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allows multiple perspectives of personal information

The role of profiles in the semantic web – Who is managing you as an entity on the web?

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The value of self-declared interests, preferences, and expertise

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The value of messaging-derived interests, preferences, and expertise

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The inclusion of RSS and weblogs to circulate and collect content and context about individuals

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1. Profiles: example of self-attributed user profile

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2. Search: Comprehensive Search on People at Companies

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2. PeopleSearch relevance •

Personalized results based on proximity in your network – The person you are looking for is more likely to be connected to your social network than not

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Relevance based on number of connections – Google changed page rank relevance to “most connected” – People are more relevant the more connected they are...to you

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Results based on match in profiles – Declared Interests, Preferences, and Needs – News and Event-related context sensitivity

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3. Social Network data about who-knows-whom provides a new source of context about individuals •

My Personal context is defined by my own relationship circles

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My context can be derived from analysis of my social cohort

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Profiling by association (whom you know defines your attributes)

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Cohorts create indicators for content relevance – In information search – In semantic routing of content – Targeting of media and advertising – Suggested distribution in publish/subscribe networks

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“Connectedness” to circles of experts can denote subject familiarity

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All this governed by personal privacy rules with permissions

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4. Social Networks create New Models for Messaging and Communication •

Creates context for communication – Social networks cut through the chaos of large collectives and create stronger paths of connection and provide for the creation of subgroups

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Trusted networks create implied endorsement even after crossing multiple degrees

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Position in network may provide source qualification for new messages

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Audit trails based on relationship or membership in topical communities qualifies contact and increases comfort with “strangers”

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Declared connections provide the basis for two-way permissions and knowledge sharing

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Enabling Trusted Messaging and Collaboration •

Like IM, pre-authorized two-way channels based on your SN circles

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Unlike email, not openly accessible to anyone who can determine address

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Validated senders=those in my network, or those who can pass through my network

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Social Networks for Dissemination: allows discovery and validation of whom-to-message in extended trusted networks

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The end of the General Inbox – Prioritization and organization by strength of relationship – Pre-qualification of new senders based on referral endorsements or ties to reference networks – Inboxes based on topical or community-of-interest networks 19 Spoke Software - Confidential

Eventually, Social Network Analysis is applicable to other messaging/data exchanges 1. SN currently focused on web interactions/transactions and email 2. Next: Instant Messaging 3. Then: Voice over IP 4. Social Network Analysis and the Semantic Web 1. Auto-creation or nomination of RDF assertions about individuals •

Based on user profiles

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Based on cohorts

2. Metadata about software systems interactions •

Relationship analysis of software systems’ behavior as nodes in the social network of web services (software agents analogous to humans as nodes in the graph)

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What is the SOR of this agent with all others like X, where SOR is a measure based on successful transactions?

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Finally, reputation tracking of software agents representing humans.... 20 Spoke Software - Confidential